Dragan Pamucar
54080216100
Publications - 129
Analysis of magnetohydrodynamic flow of Jeffrey-Hamel fluid in convergent/divergent channels using the numerical algorithm
Publication Name: Kuwait Journal of Science
Publication Date: 2026-01-01
Volume: 53
Issue: 1
Page Range: Unknown
Description:
This study explores the magnetohydrodynamic (MHD) flow of a Jeffrey-Hamel fluid within a convergent/divergent channel, a scenario relevant to both physical and biological sciences. The flow dynamics between nonparallel inclined walls are governed by highly nonlinear differential equations derived through conservation laws and similarity transformations. By applying similarity transformations, the governing partial differential equations (PDEs) are converted into ordinary differential equations (ODEs). The NDSolve approach is then utilized to obtain numerical solutions for these equations. A comparison with existing methods in the literature confirms the accuracy and reliability of the results. Additionally, the impact of various dimensionless physical parameters, such as the influence of the magnetic parameter, angle alpha, and the Deborah number on the velocity profile is investigated. The parameters angle alpha, Eckert number, and volume friction are examined on the temperature profile, followed by a detailed discussion of the findings.
Open Access: Yes
Tackling energy poverty with renewable energy Projects: Fuzzy decision support system based on virtual and real experts
Publication Name: Renewable Energy
Publication Date: 2026-01-01
Volume: 256
Issue: Unknown
Page Range: Unknown
Description:
Energy poverty is a serious problem that increases economic inequalities, especially because individuals living in low-income areas have difficulty accessing energy. The development of renewable energy projects (REP) plays a critical role in reducing energy poverty. However, there is considerable uncertainty in determining strategies that will increase the effectiveness of REP to solve the problem of energy poverty. The purpose of this paper is to identify significant strategies to improve REP for the effective management of energy poverty problems by establishing a novel model. First, dimension reduction methodology is considered to calculate the importance of decision makers. The second stage includes prioritization of criteria using p,q-Spherical fuzzy (SFS) analytic hierarchy process (AHP). The final stage focuses on ranking of renewable energy investment (REI) alternatives using p,q-SFS weighted aggregated sum product assessment (WASPAS). The contribution of this paper to the literature is the determination of critical indicators that will increase the performance of REI to reduce the energy poverty problem with an original and comprehensive decision-making model. Creating a virtual expert is the main superiority of this proposed model. With the help of this issue, it can be possible to reach a sufficient number of experts. Hence, a more diverse and comprehensive evaluation can be conducted. The findings denote that start-up costs and geographical conditions have the highest significance to improve REP for the aim of minimizing energy poverty problem. Rooftop solar panels and micro wind turbines are also found as the most essential REI strategies.
Open Access: Yes
A novel Gustafson–Kessel based clustering algorithm using n-Pythagorean fuzzy sets
Publication Name: Systems and Soft Computing
Publication Date: 2025-12-01
Volume: 7
Issue: Unknown
Page Range: Unknown
Description:
The Gustafson–Kessel (GK) algorithm, an extension of the fuzzy c-means (FCM) clustering method, effectively handles non-spherical clusters but struggles with uncertainty in membership assignments. To address this limitation, we propose the n-Pythagorean Fuzzy Gustafson–Kessel (n-PyGK) algorithm, which incorporates an inherent hesitation degree to enhance clustering performance. The proposed algorithm is evaluated on both synthetic and real-world datasets, including the Iris dataset, using nine clustering metrics. We analyze its behavior under varying parameter settings and compare its performance with traditional clustering algorithms. Experimental results demonstrate that n-PyGK offers improved clustering accuracy and greater flexibility in parameter selection, enabling optimal performance for specific clustering indices.
Open Access: Yes
Decision-analytics-based electric vehicle charging station location selection: A cutting-edge fuzzy rough framework
Publication Name: Energy Reports
Publication Date: 2025-12-01
Volume: 14
Issue: Unknown
Page Range: 711-735
Description:
Electric vehicles are of great significance in supporting sustainable transportation and sustainability. In parallel with the increasing demand for such vehicles worldwide, the electric vehicle charging stations (EVCSs) market has grown dramatically. The study presents a practical model for selecting EVCS sites integrating multi-criteria decision-making (MCDM), fuzzy, and rough sets. The research aims to bridge the gap in evaluating EVCS locations by leveraging the superiorities of fuzzy and rough set theories to address vagueness effectively. Firstly, assessment criteria cover the environment, economic, technology, and social drivers. Secondly, a fuzzy Defining Interrelationships Between Ranked criteria (F-DIBR) model is applied to determine the weight values of siting factors. Last, for the first time, the Mixed Aggregation by COmprehensive Normalization Technique (MACONT) with hybrid fuzzy rough numbers (FRN-MACONT) model is proposed to obtain the ranking results. Further, a new approach for defining hybrid fuzzy rough numbers is suggested, based on an improved methodology for determining rough numbers' lower and upper limits, allowing consideration of mutual relations between a set of objects and flexible representation of rough boundary intervals depending on the dynamic environmental conditions. The study's novelties reside in deciding the importance of the driving forces used in determining the EVCS site location with a novel method, F-DIBR, and selecting the optimal site with a new FRN-MACONT approach. The results show that “economy” is the most significant criterion, whereas “system reliability” is the most critical sub-criterion. The findings also indicate that the Konak territory performs the best, whereas the Cigli territory is the second best. Comprehensive sensitivity analysis verifies the proposed framework's validity, robustness, and effectiveness. As per the research findings and analyses, some managerial implications are further discussed. The approach introduced has the potential to contribute to the green transport literature.
Open Access: Yes
Multi-Attribute Decision-Making Technique using Bipolar Linear Diophantine Fuzzy Hypersoft Set
Publication Name: Journal of Fuzzy Extension and Applications
Publication Date: 2025-12-01
Volume: 6
Issue: 4
Page Range: 727-748
Description:
The state of bipolarity plays a major role in any circumstance due to the involvement of fors and againsts for each condition. This research enhances some rudimentary operations, propositions, and valuable theorems based on the recently developed aspect called the bipolar linear diophantine fuzzy hypersoft set. The choice of the individual without restriction enhances the technique. In addition, all its crumbles are curtailed with more flexibility. Railways are easily accessible for all classes of people and require a guarded journey. Currently, rail accidents are a major controversy all over the world as they kill many and injure riskily. For this reason, an algorithm is expanded to elect an effective crash-evasive rail carriage equipped with ultrasonic sensors to detect the range of the hindrance and brake controls. The study aims to gain additional insight and solve the critical issue of railway safety, particularly regarding recent incidents that have placed passengers at risk worldwide. The initiative aims to develop a highly effective crash-evasive rail carriage system by extending the capabilities of existing algorithms. This technology aims to minimize the incidence of rail accidents by selecting an ideal system based on the sensing range of sensor and brake control mechanisms. The study further distinguishes its innovations to set theoretic operations within the framework plays a vital role in strengthening procedures for making choices and guaranteeing the accuracy of the proposed solution. The recommended strategy not only confronts current safety concerns but also sets the foundation for future technological evolution in the railway sector.
Open Access: Yes
Proximal Policy Optimization-based Task Offloading Framework for Smart Disaster Monitoring using UAV-assisted WSNs
Publication Name: Methodsx
Publication Date: 2025-12-01
Volume: 15
Issue: Unknown
Page Range: Unknown
Description:
Unmanned Aerial Vehicles (UAVs) are increasingly employed in Wireless Sensor Networks (WSNs) to enhance communication, coverage, and energy efficiency, particularly in disaster monitoring and remote surveillance scenarios. However, challenges such as limited energy resources, dynamic task allocation, and UAV trajectory optimization remain critical. This paper presents Energy-efficient Task Offloading using Reinforcement Learning for UAV-assisted WSNs (ETORL-UAV), a novel framework that integrates Proximal Policy Optimization (PPO) based reinforcement learning to intelligently manage UAV-assisted operations in edge-enabled WSNs. The proposed approach utilizes a multi-objective reward model to adaptively balance energy consumption, task success rate, and network lifetime. Extensive simulation results demonstrate that ETORL-UAV outperforms five state-of-the-art methods Meta-RL, g-MAPPO, Backscatter Optimization, Hierarchical Optimization, and Game Theory based Pricing achieving up to 9.3 % higher task offloading success, 18.75 % improvement in network lifetime, and 27 % reduction in energy consumption. These results validate the framework's scalability, reliability, and practical applicability for real-world disaster-response WSN deployments. • Proposes ETORL-UAV: Energy-efficient Task Offloading using Reinforcement Learning for UAV-assisted WSNs • Leverages PPO-based reinforcement learning and a multi-objective reward model • Demonstrates superior performance over five benchmark approaches in disaster-response simulations
Open Access: Yes
Digital transformation project risks assessment using hybrid picture fuzzy distance measure-based additive ratio assessment method
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
Digital transformation (DT) has become vital for companies trying to remain competitive in the recent ever-changing technological environment. DT is the integration of digital technologies into all disciplines of business from regular activities to strategic decision making. Risk management planning requires projects to assess possible risks that may negatively or positively affect a DT project. The purpose of the study is to introduce a hybridized decision support system (DSS) by combining the distance measure, ranking comparison (RANCOM) model and additive ratio assessment (ARAS) approach in the context of a picture fuzzy set (PFS). In this framework, the decision experts’ significance values are computed using a picture fuzzy score function-based formula. With the combination of objective weight using distance measure and subjective weight through the RANCOM model, a combined weight-determining approach is developed to determine the significance values of considered DT risks under picture fuzzy environment, while a hybrid ARAS model is developed to evaluate and rank DT projects from the risks perspective. To exhibit the feasibility of the introduced framework, a case study of a DT projects assessment problem is discussed in the context of picture fuzzy sets. A sensitivity study is also discussed over different values of the strategy coefficient, which confirms the strength of the proposed model. Further, a comparison with the existing picture fuzzy information-based methods is presented to prove the robustness of the developed decision-making framework.
Open Access: Yes
Driving sustainable hydroelectric investments: Leveraging two-step logarithmic normalization for sustainable investment prioritization
Publication Name: Energy Reports
Publication Date: 2025-12-01
Volume: 14
Issue: Unknown
Page Range: 2110-2122
Description:
Hydroelectric energy investments involve substantial techno-economic risks that can increase costs and undermine economic sustainability if not properly managed. However, the literature lacks comprehensive studies addressing these risks. This study proposes a novel decision-making model to identify and prioritize strategies for effective risk management in hydroelectric projects. The model integrates z-scoring for expert selection, the Criteria Importance Assessment (CIMAS) method for weighting criteria, and the Alternative Ranking using Two-Step Logarithmic Normalization (ARLON) method for ranking EU-15 countries according to their strategies. Pythagorean fuzzy numbers are incorporated to better handle uncertainty and improve evaluation accuracy. Results indicate that challenges in adopting new technologies and grid integration issues are the most influential risk factors. The findings provide actionable insights for policymakers and investors to enhance the sustainability and efficiency of hydroelectric energy investments. Policymakers should implement targeted incentives and regulatory frameworks to accelerate technology adoption and address grid integration challenges in hydroelectric projects. Strategic planning should prioritize infrastructure modernization, cross-border energy cooperation, and capacity-building programs to enhance sector resilience and investment security.
Open Access: Yes
A novel numerical investigation of fiber Bragg gratings with dispersive reflectivity having polynomial law of nonlinearity
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
Fiber Bragg gratings represent a pivotal advancement in the field of photonics and optical fiber technology. The numerical modeling of fiber Bragg gratings is essential for understanding their optical behavior and optimizing their performance for specific applications. In this paper, numerical solutions for the revered optical fiber Bragg gratings that are considered with a cubic-quintic-septic form of nonlinear medium are constructed first time by using an iterative technique named as residual power series technique (RPST) via conformable derivative. The competency of the technique is examined by several numerical examples. By considering the suitable values of parameters, the power series solutions are illustrated by sketching 2D, 3D, and contour profiles. The results obtained by employing the RPST are compared with exact solutions to reveal that the method is easy to implement, straightforward and convenient to handle a wide range of fractional order systems in fiber Bragg gratings. The obtained solutions can provide help to visualize how light propagates or deforms due to dispersion or nonlinearity.
Open Access: Yes
Integration of data-driven T-spherical fuzzy mathematical models for evaluation of electric vehicles: Response to electric vehicle market demands
Publication Name: Renewable and Sustainable Energy Reviews
Publication Date: 2025-11-01
Volume: 223
Issue: Unknown
Page Range: Unknown
Description:
The rapid growth of the electric vehicle (EV) market necessitates advanced multi-criteria decision-making (MCDM) frameworks capable of integrating diverse quantitative and qualitative factors under uncertainty. Traditional MCDM approaches often struggle to capture the complexity and imprecision inherent in EV evaluations, particularly in dynamic contexts like India. To address this gap, this study proposes the T-Spherical Fuzzy (T-SF) MARCOS and T-SF MOORA methods, which utilize T-Spherical Fuzzy Numbers (T-SFNs) to enhance decision precision. T-SFNs extend conventional fuzzy models by independently incorporating degrees of membership, non-membership, and hesitation, enabling a more granular and realistic modeling of expert judgments. In the methodological construction, numerical criteria (e.g., battery capacity, charging time) are directly incorporated, while qualitative criteria (e.g., safety, comfort) are initially evaluated by four domain experts through linguistic assessments, subsequently transformed into T-SFNs for integrated evaluation and accurate criteria weighting. The developed models are then employed to rank ten EV alternatives across 21 comprehensive technical and consumer-centric criteria. Comparative analysis shows that T-SF MARCOS and T-SF MOORA achieve superior ranking accuracy, with a high mutual Pearson correlation of 0.71, while traditional SF methods like SF-WSM and SF-WASPAS exhibit negative correlations of −0.43 and −0.42, respectively. Sensitivity analyses—covering variations in criteria weights and additional criteria integration—confirm the robustness and stability of the frameworks, with rank reversal rates remaining below 10 % across all scenarios. This study presents a technically resilient, uncertainty-aware evaluation framework, offering strategic insights for advancing consumer-centric EV development.
Open Access: Yes
A state-of-the-art review on machine learning techniques for driving behavior analysis: clustering and classification approaches
Publication Name: Complex and Intelligent Systems
Publication Date: 2025-09-01
Volume: 11
Issue: 9
Page Range: Unknown
Description:
Smart mobility has ushered in advanced sensing technologies. These, together with high‑level data analytics, are revolutionizing how we analyze driving behavior. Excellent performance in dealing with real-world, high-technology complexities for machine learning has made wide enthusiasm to utilize them to study driver behavior. This article gives a thorough overview of the important machine learning methods—especially clustering and classification techniques—that help analyze complex driving behaviors, predict fuel and energy use, and improve vehicle safety systems. The review specifically explains unsupervised methods like fuzzy c-means, k-means, and density-based spatial clustering of applications with noise, as well as supervised techniques such as artificial neural networks, k-nearest neighbors, and support vector machines. Also, this review discusses the integration of clustering and classification techniques with hybrid deep learning models, and examines their applications in eco-driving, energy forecasting, and intelligent transport systems while offering novel findings that contribute to more sustainable mobility. Emphasis is placed on how these methods transform vast, heterogeneous driving data into actionable insights that support real-time monitoring and personalized feedback for eco-driving and smart transportation applications. Finally, current benefits and barriers, and future research opportunities and challenges in integrating machine learning into intelligent transportation systems are reviewed. The potential to advance to safer, better, and more sustainable forms of mobility is emphasized.
Open Access: Yes
Evaluation of Mobile Applications for Small Farms Using Fuzzy Methods
Publication Name: International Journal of Research in Industrial Engineering
Publication Date: 2025-09-01
Volume: 14
Issue: 3
Page Range: 426-444
Description:
This paper offers a practical model to help farmers choose the most suitable mobile application for their specific needs, improving decision-making processes in adopting agricultural technology. Given the wide range of applications available on the market, the need to select the one that best improves agricultural production motivated the research in this paper. To simplify the decision-making process for farmers, a methodology that applied the fuzzy approach was developed. Based on this, this research aimed to evaluate and identify mobile applications most suitable for the Farmino farm using a Multi-Criteria Decision-Making (MCDM) approach. A decision-making model that includes ten criteria and several mobile applications was applied. Farm employees, who are the intended users of these applications, evaluated the criteria and applications using linguistic terms. The methods of fuzzy Simple Weight Calculation (SiWeC) and fuzzy Logarithmic Percentage Change-Driven Objective Weighting (LOPCOW) were used to determine the weight of the criteria. These methods revealed that the criterion "Data accuracy" was more important than the others, while the importance of the other criteria was less. Finally, the fuzzy method Multi-Attributive Border Approximation Area Comparison (MABAC) was used to rank mobile applications, and the results showed that the A4 mobile application ranked highest, making it the best choice for Farmino farm.
Open Access: Yes
Prediction of possible tornado strike using complex m-polar fuzzy information based on Dombi operators
Publication Name: Ain Shams Engineering Journal
Publication Date: 2025-08-01
Volume: 16
Issue: 8
Page Range: Unknown
Description:
Tornados are extremely catastrophic, and the global effect of natural calamities like tornados is enormous and needs prompt and effective management. We can tackle this problem by using measures like multi-criteria decision-making (MCDM) to identify high-risk areas of a potential tornado strike. We frequently use MCDM techniques to solve the complexities and uncertainties of modern-era problems. We present a study that builds a prediction model by combining the Dombi aggregation operator with a complex m-polar fuzzy set (CmFS) to accurately guess when a tornado will hit. Our proposed model determines an expert panel, criteria, and a set of alternatives after identifying the problem. We create summed-up decision matrices using complex m-polar fuzzy Dombi aggregation operators (CmFDAO) after experts evaluate criteria and options. The algorithm then presents the best option with the help of a final decision score matrix. Our model uses a set of eight meteorological elements and eight experts to assess four possible tornado locations and pinpoint an area with a high risk of tornado strikes. The results generated by our aggregation operator set demonstrate that our proposed method for handling complex and multi-polar data is concise and efficient when compared to other sets. This early prediction highlights the potential of significant risk reduction to the environment and human life due to catastrophic events like tornados by enhancing early warning systems and effective emergency management.
Open Access: Yes
Integrating Artificial Intelligence into Fuzzy Decision Analytics: A Novel Approach to Mitigating Stereotype Threat in Sustainable Business Environments
Publication Name: Journal of Fuzzy Extension and Applications
Publication Date: 2025-06-01
Volume: 6
Issue: 2
Page Range: 371-390
Description:
Preventing the threat of stereotyping is critical for business performance improvements. Because of this situation, businesses must take the necessary precautions. However, these actions have an impact on cost increase for the businesses. The number of studies in the literature performing priority analysis for these factors is quite limited. This situation increases the need for a new study that prioritizes the analysis of these variables. Accordingly, this study aims to evaluate the factors against the stereotype threat in the sustainable business environment. An artificial intelligence model is implemented in the first stage to weigh the experts. In the following stage, selected criteria are evaluated with the help of T-Spherical fuzzy DEMATEL. Thirdly, a comparative analysis was performed using different values. Finally, selected industries are ranked by Spherical Fuzzy RATGOS with respect to the stereotype threat. The weights of the experts can be identified in the analysis process. This situation has a strong contribution to the effectiveness of the findings. It is concluded that training activities are critical to minimizing the threat of stereotypes in companies.
Open Access: Yes
Data mining applications in risk research: A systematic literature review
Publication Name: International Journal of Knowledge Based and Intelligent Engineering Systems
Publication Date: 2025-05-01
Volume: 29
Issue: 2
Page Range: 222-261
Description:
Despite the rising literature on data mining (DM) approaches, there is a lack of a complete literature review and categorization system within risk research. This paper presents the first recognized academic literature review on the application of data mining tools in risk research provides an up-to-date SCOPUS literature database. Based on bibliometric analysis, 5422 papers related torisk were identified from a total of 77,410 studies on data mining and thoroughly analyzed. Each of the selected 5422 papers was classified into four risk categories: global risk, public health risk, molecular and biomedical risk, and pharmaceutical risk. Each primary risk category was further subdivided to highlight the specific research focuses within each domain. Global risks encompass business, environmental, and social risks. Scholars have predominantly focused on the banking, market, and construction sectors within business risk, while environmental risk includes catastrophe-related risks. Social risks encompass areas such as education, traffic safety, and transportation concerns. Clinical data is usually employed in public health risk research, while various radiomic databases are utilized in genetic and molecular biology research. In pharmaceutical research, DM is primarily used to detect adverse drug effects. According to the findings of this review, the fields of computer science and medicine received the most significant research attention. The review also discusses limitations and provides a roadmap to guide future research, aiming to enhance knowledge development related to the application of data mining techniques in risk-related studies.
Open Access: Yes
ENERGY MANAGEMENT POLICY SELECTION IN SMART GRIDS: A CRITIC-CoCoSo METHOD WITH Lq* q-rung ORTHOPAIR MULTI-FUZZY SOFT SETS
Publication Name: Applied Engineering Letters
Publication Date: 2025-03-01
Volume: 10
Issue: 1
Page Range: 35-47
Description:
In response to the energy crisis and the global push for sustainability, modern power grids are increasingly integrating renewable energy, plug-in electric vehicles, and energy storage systems. This evolution demands an advanced energy management system capable of handling the variability of renewable resources, uncertainties in electric vehicle performance, fluctuating electricity prices, and dynamic load conditions. To address these challenges, our study introduces a novel decision-making framework that leverages a new score function for comparing q-rung orthopair multi-fuzzy soft numbers. This approach employs the Criteria Importance Through Inter-criteria Correlation (CRITIC) method to determine objective weights while simultaneously incorporating subjective preferences through an integrated weighting scheme. The framework is further enhanced by applying the Combined Compromise Solution (CoCoSo) method within the Lq* q-rung orthopair multi-fuzzy soft decision-making structure to select optimal energy management policies. Extensive sensitivity analysis confirms the robustness and effectiveness of the proposed methodology, offering a promising solution for efficient energy management in modern power systems.
Open Access: Yes
Economic and Technical Assessment of the Chinese Plum Varieties Using Multi-Criteria Analysis Methods
Publication Name: Agricultural Research
Publication Date: 2024-01-01
Volume: Unknown
Issue: Unknown
Page Range: Unknown
Description:
Globally, plums are among the most grown and consumed fruit species. Considering China as the leader in plum production, there are intentions to introduce the usually grown plum varieties in China into the area of Western Balkan (WB). It is expected that this action will trigger knowledge and technological transfer, as well as improvement in reached profitability. The suitability of available seven plum varieties (Superior, Skoroplodnaya, Eagle Dream, Sissy, Manchu Beauty, Golden ball and Red ball) to production and market conditions of Western Balkan are analysed by experts’ assessment of offered alternatives according to predefined sets of (sub)criteria (economic and technological). In line with the main goal of the research support in the decision-making process, i.e. ranking the observed plum varieties is done according to the selected multi-criteria method, in this case, fuzzy DNARAS multi-criteria decision-making method (Double Normalization Fuzzy Additive Ratio Assessment). Previous assessments of observed plum varieties towards the predefined sets of criteria are done by engaged national experts focused on fruit growing. Derived research results show that the plum variety Sissy could perfectly fit the WBs fruit production sector, while the variety Superior possess the lowest growing potential among the assessed plum varieties. Research originality lies in the fact that assessment and ranking of selected plum varieties have been done with the fuzzy DNARAS multi-criteria decision-making method, a method that shows a higher level of stability compared to other similar methods. Performing the quasi-experiment under the expert’s opinions and suggesting the plum varieties that could correspond to adequate growing alternatives simultaneously represents the initial stage in multi-year field experiments linked to the introduction of marked varieties into the WBs.
Open Access: Yes
The Race to Sustainability: Decoding Green University Rankings Through a Comparative Analysis (2018–2022)
Publication Name: Innovative Higher Education
Publication Date: 2025-02-01
Volume: 50
Issue: 1
Page Range: 241-275
Description:
This study investigates the evolving landscape of green universities by analyzing and comparing rankings from 2018 to 2022. It expands beyond the single score offered by the UI GreenMetric, employing Multi-Criteria Decision-Making (MCDM) techniques to evaluate universities from diverse perspectives. Focusing on the top 50 universities from 2022, the study assesses their performance across six key criteria: setting and infrastructure, energy and climate change, waste, water, transportation, and education and research. Various MCDM methods (LOPCOW MEREC, CoCoSo, CRADIS, EDAS, MABAC, MAIRCA, and MARCOS) are implemented, revealing how they prioritize different aspects of sustainability. Furthermore, the study examines the correlation between rankings and employs the COPELAND aggregation approach to derive a unified ranking. This investigation not only contrasts MCDM outcomes with the UI GreenMetric’s total score-based rankings but also illuminates the relative significance of each criterion and its variation across weighting techniques. Additionally, the study delves into the temporal dynamics of university rankings, offering insights into institutional performance across different years.
Open Access: Yes
Normal wiggly probabilistic hesitant fuzzy-based TODIM approach for optimal solid waste disposal method selection
Publication Name: Heliyon
Publication Date: 2025-01-30
Volume: 11
Issue: 2
Page Range: Unknown
Description:
The normal wiggly probabilistic hesitant fuzzy set (NWPHFS) enhances the conventional probabilistic hesitant fuzzy set (PHFS) by capturing not only explicit probabilistic information but also critical underlying details that may be hidden in the original inputs provided by decision-makers (DMs). This paper introduces a novel extension of the Tomada de Decisão Interativa Multicritério (TODIM) method, called the normal wiggly probabilistic hesitant fuzzy TODIM (NWPHFT) method based on the proposed distance measures of NWPHFSs. Initially, two novel basic operations over NWPHFSs—the subtraction and division operations—are defined. Additionally, several distance measures specific to normal wiggly probabilistic hesitant fuzzy sets are developed, and their properties are thoroughly examined. Furthermore, for scenarios where the weights of criteria are partially or completely unknown, two optimization models are established to determine these weights using the maximizing deviation approach and the Lagrange function technique, respectively. Next, the traditional TODIM approach is extended to develop the NWPHFT for addressing MCDM problems by utilizing the proposed distance measures and criteria weight determination models. The proposed method is then applied to a problem related to selecting solid waste disposal methods to demonstrate its practical applicability. Finally, comprehensive sensitivity analyses and comparisons are conducted to illustrate the stability and effectiveness of the proposed approach.
Open Access: Yes
A novel Complex q-rung orthopair fuzzy Yager aggregation operators and their applications in environmental engineering
Publication Name: Heliyon
Publication Date: 2025-01-15
Volume: 11
Issue: 1
Page Range: Unknown
Description:
Improving human health and comfort in buildings requires efficient temperature regulation. Temperature control system has a significant contribution in minimizing the impact of climate change. Temperature control system is used in industry to control temperature. The polar form of complex Pythagorean fuzzy set is a limited notion because when decision makers take the value for membership degree as 0.71+ι0.81 then we can observe that the basic condition for complex Pythagorean fuzzy set fails to hold that is r=0.712+0.812=1.3661∉[0,1]. Moreover, we can observe that the Cartesian form of a complex Pythagorean fuzzy set is also a limited notion because it can never discus advance data. Hence keeping in mind these limitations of the existing notions, in this article, we have explored the Cartesian form of a complex q-rung orthopair fuzzy set. Moreover, we have developed the Yager operational laws based on a Cartesian form of complex q-rung orthopair fuzzy set. We have introduced aggregation theory named complex q-rung orthopair fuzzy Yager weighted average and complex q-rung orthopair fuzzy Yager weighted geometric aggregation operators in Cartesian form. Based on these aggregation operators, we have initiated a multi-attribute group decision-making (MAGDM) approach to define the reliability and authenticity of the developed theory. Furthermore, we have utilized this device algorithm in the selection of a temperature control system. The comparative study of the delivered approach shows the advancement and superiority of the delivered approach.
Open Access: Yes
Prioritization of Geothermal Energy Systems for Industrial Applications by Using Hesitant Bipolar Fuzzy Multi-Criteria Decision-Making Technique Based on Dombi Operators
Publication Name: Contemporary Mathematics Singapore
Publication Date: 2025-01-01
Volume: 6
Issue: 4
Page Range: 4033-4059
Description:
The proposed research fills a significant gap in the decision-making technique for evaluating geothermal energy systems in industrial processes by introducing a new approach involving Hesitant Bipolar Fuzzy (HBF) Sets (HBFSs) with Dombi operators. The existing literature has mostly focused on uncertainty only, overlooking the aspect that decisions tend to be imprecise, bipolar, and hesitant in reality. To overcome this gap, we first introduce Dombi operators in the context of HBFSs, thereby improving the parametric flexibility in handling more complex uncertain information. Based on these operators, we establish an HBF Multi-Criteria Decision-Making (MCDM) method for the ranking of geothermal energy systems. The applicability of our proposed methodology for prioritizing different types of geothermal energy systems for industrial applications is illustrated in a detailed case study that supports the theoretical framework. The benefit of the suggested method is also supplemented by the comparison of the proposed method with the previous methods and evidence of the capability to handle uncertainty and make more precise and confident decisions. This study offers an important theoretical as well as practical contribution to decision-making practices and the choice of sustainable energy systems for geothermal energy options under uncertainty, offering decision-makers a robust framework of analysis. Moreover, we have the following key findings or outcomes of proposed research. • Development of HBF Dombi Weighted Averaging (HBFDWA) operators. • Development of HBF Dombi Ordered Weighted Averaging (HBFDOWA) operators. • Development of HBF Dombi Weighted Geometric (HBFDWG) operators. • Development of HBF Dombi Ordered Weighted Geometric (HBFDOWG) operators. • A case study is performed based on the developed operators to rank geothermal energy systems. • A comparative analysis is performed to show the superiority of the proposed approach. • A sensitivity analysis is discussed to show the influences of the parameter.
Open Access: Yes
Identification of Delay-Tolerant Networking by Employing MABAC Technique Based on Bipolar Complex Fuzzy Dombi Heronian Mean Operators
Publication Name: Contemporary Mathematics Singapore
Publication Date: 2025-01-01
Volume: 6
Issue: 3
Page Range: 3562-3612
Description:
This work proposes a hybrid decision making model for dynamic and irregularly connected communication systems called Delay-Tolerant Networks (DTNs). A resilient and adaptable network allows for communication in an environment where traditional networks may fail to operate effectively. The main significance of this system is that it is commonly utilized in such scenarios where traditional networks are impractical, such as remote areas, disaster-stricken regions, space missions, and military operations. The proposed model includes the “Multi-Attributive Border Approximation Area Comparison” (MABAC) method, together with Bipolar Complex Fuzzy Dombi Heronian Mean (BCFDHM) operators. To take the positive as well as negative attributes’ evaluations into consideration in complicated fuzzy environments, we use an enriched aggregation structure for the criteria, which incorporates the relationship between criteria through the Heronian mean function. Due to this, the MABAC technique within BCF information is more advanced and better than classical MABAC techniques in various models. After that, with the help of these enriched aggregation structures, we successfully identify and rank alternatives for DTN in an uncertain, imprecise, and bipolar condition. By employing the MABAC technique for the DTN system, we find the best and better alternative to the DTN system, which is Ã4 as mentioned below in section 4. At last, we compare our initiated work with many existing theories to prove the authenticity of the suggested work.
Open Access: Yes
Energy Storage System Selection for AI-Controlled Microgrids Using Complex Hesitant Fuzzy MCDM Approach Based on Dombi Operators
Publication Name: Contemporary Mathematics Singapore
Publication Date: 2025-01-01
Volume: 6
Issue: 3
Page Range: 3269-3300
Description:
The current definition of the Complex Hesitant Fuzzy Set (CHFS), derived from the Ramot form of complex numbers, cannot process information as in Tamir’s complex fuzzy form. We have data with uncertainty and extra information that cannot be described by any other structure than Tamir’s complex fuzzy form. Hence, in this article, we initiated the idea of CHFS based on Tamir’s complex fuzzy form and established its operational laws. Since Decision-Making (DM) theory is central to nearly all disciplines, we have proposed a novel complex hesitant fuzzy Multi-Criterion Decision-Making (MCDM) model. This method can handle all sorts of real-life MCDM problems, where the data contains uncertainty, hesitancy, and extra fuzzy information. While developing this method, we also develop and apply Dombi aggregation operators in this manuscript. After that, we discussed a case study that concerns energy storage system selection for AI-controlled microgrids and discussed how the theory we have developed can be applied to real-world challenges. Last, we conferred on how this proposed theory is superior to other theories and why it should be adopted.
Open Access: Yes
Decision-Analytics-Based Stock Selection: A Fuzzy Aczel–Alsina Ordinal Priority Approach
Publication Name: International Journal of Fuzzy Systems
Publication Date: 2025-01-01
Volume: Unknown
Issue: Unknown
Page Range: Unknown
Description:
In today’s competitive environment, evaluating and selecting stocks for portfolio optimization is a critical challenge for investors, especially under conditions of uncertainty. Traditional approaches often fail to address the complexities of multi-criteria decision-making (MCDM) in real-world investment scenarios. This study introduces a novel fuzzy Ordinal Priority Approach based on Aczel–Alsina weighted evaluation (OPA-AAWE) to tackle the portfolio selection problem. Taking into account seven financial performance criteria, the model was applied to 374 stocks listed on the Istanbul Stock Exchange for a period of 12 months. The findings demonstrate that the proposed methodology effectively handles uncertainty, offers flexibility in decision-making, and identifies the most optimal portfolios. Sensitivity analysis further confirms the robustness and reliability of the model. These results highlight the practical applicability of the fuzzy OPA-AAWE framework in real-world investment decision-making, offering investors a comprehensive tool for improved portfolio selection.
Open Access: Yes
Q-Fractional Hesitant Fuzzy Sets and Their Correlation Coefficients: Multi-Criteria Decision Making Technique for Selection of Agricultural Land to Cultivate Apples Crops
Publication Name: IEEE Access
Publication Date: 2025-01-01
Volume: 13
Issue: Unknown
Page Range: 134057-134069
Description:
The q-Fractional Fuzzy Sets (q-FrFSs) offers information in Membership Grade (MG) and Non-membership Grade (NMG) of an object; however, both grades have the hesitancy factor because complex information usually does not give single MG and single NMG. Therefore, in this study we initiate the concept of q-Fractional Hesitant Fuzzy Sets (q-FrHFSs) and its basic properties. In q-FrHFSs not only hesitancy factor is taken into account but it also consider all possible values of uncertainties in {0,1}× {0,1}. Thus Correlation Coefficients (CCs) on q-FrHFSs are necessary to cope uncertain information with hesitancy, MGs and NMGs. In this study we introduce two types of CCs namely CCs on q-FrHFSs and weighted CCs on q-FrHFSs. We investigate underlying properties of these CCs and give a MCDM method on q-FrHFSs environment. We consider an application of our method to agricultural land selection across a set of cities for cultivation of apples crop. At the end, we compare our method of q-FrHFSs to some existing frameworks.
Open Access: Yes
Evaluation of Fim Performance under Merger and Acquisition Effect: An Integrated LOPCOW-PIV Approach
Publication Name: Decision Making Applications in Management and Engineering
Publication Date: 2025-01-01
Volume: 8
Issue: 1
Page Range: 588-614
Description:
Merger and Acquisition (MA) is one of the critical strategic decisions for the firms that impact the existence and growth of the organizations. The present paper undertakes the context of MA and aims to compare performance of some of the recent acquirers using fundamental financial ratios and market indicators. The study period spans over four consecutive financial years (FY 2019-20 to FY 2022-23). To carry out a comprehensive evaluation of firm performance, the current work uses a multi-criteria decision-making (MCDM) framework of LOPCOW (Logarithmic Percentage Change-driven Objective Weighting) and PIV (Proximity Index Value) methods. To aggregate the year wise rankings of the firms, Borda Count and Rank Index Method (RIM) is used. It is observed that ROE (C1), Net Profit Margin (C4) and EPS (C9) obtained the highest weights over the study period. On aggregate, we find that Infosys (A4), HUL (A3) and ITC (A1) show top performance while Vodafone (A11), PVR Inox (A9) and IDFC First Bank (A13) remain in the bottom bracket. The comparative analysis with other MCDM models reveals that the ranking results are consistent while the outcome of the sensitivity analysis reflects the stability. The present work provides a new perspective to the investors, policy makers and analysts.
Open Access: Yes
Advancing PFMEA Decision-Making: FRADAR Based Prioritization of Failure Modes Using AP, RPN, and Multi-Attribute Assessment in the Automotive Industry
Publication Name: Tehnicki Glasnik
Publication Date: 2025-01-01
Volume: 19
Issue: 3
Page Range: 442-451
Description:
This research proposes a novel way to improve Process Failure Modes and Effects Analysis (PFMEA) by using the Fuzzy RAnking based on the Distances And Range (FRADAR) method to prioritize activities for mitigating or eliminating failure modes in the automotive industry. The suggested approach seeks to improve classic PFMEA by using fuzzy sets to better assess risk-related criteria and their inherent uncertainty. The criteria used to prioritize actions for mitigating failure modes include the Action Priority (AP) and Risk Priority Number (RPN) approach, as well as the cost-effectiveness of actions, the time required to resolve issues, and their impact on production, all of which are assessed by a PFMEA team using predefined linguistic terms and suggestions. Applied to a case study of a Tier-1 automotive supplier, the FRADAR method effectively ranks failure modes, providing a structured and precise approach for action prioritization. The results highlight the model’s potential to enhance decision-making processes, offering a robust framework for implementing PFMEA recommendations in the automotive industry.
Open Access: Yes
Using multi-attribute decision-making technique for the selection of agribots via newly defined fuzzy sets
Publication Name: Aims Mathematics
Publication Date: 2025-01-01
Volume: 10
Issue: 5
Page Range: 12168-12204
Description:
Reference parameter mapping (passing arguments by reference) is a technique where the reference (like to find physical meaning, memory address) of a parameter is passed to a function or procedure, rather than a copy of the parameter’s value. This approach enables changes made to the parameter within the function to affect the original data. In decision-making systems, reference parameter mapping (passing arguments by reference) offers several key advantages that enhance flexibility, consistency, and efficiency. This is especially useful in scenarios where decisions are based on shared data, complex interactions, and iterative updates. In this paper, a new class of fuzzy set was introduced that is known as the (q1 ,q2 )-rung Diophantine fuzzy set, where q1 and q2 are reference parameter mappings. Most of the classical and new generalized fuzzy sets are exceptional classes of (q1 , q2 )-rung Diophantine fuzzy set ((q1 ,q2 )- RDFS) like intuitionistic fuzzy set (IFS), Pythagorean fuzzy Sets (PyFSs) and q-rung Orthopair fuzzy sets (q-ROFSs), linear Diophantine fuzzy sets (LDFS), and so on. It is commonly seen in multi-criteria decision-making (MCDM) scenarios that the presence of imprecise information and ambiguity in the decision maker's judgment affects the resolution technique. Fuzzy models that are now in use are unable to effectively manage these uncertainties to provide an appropriate balance during the decision-making process. Using control (reference) parameter mappings, (q1 , q2 )- RDFSs are potent fuzzy model that can handle these challenging problems. Two more novel ideas are presented in this work: (q1 ,q2 )-rung Diophantine fuzzy averaging and geometric aggregation operators with newly defined score and accuracy functions. An agricultural fieFS robot MCDM framework was proposed, incorporating (q1 ,q2 )-rung Diophantine fuzzy averaging and geometric aggregation operators. This strategy's efficacy and adaptability in addressing real-worFS issues were demonstrated by its application to get more benefits. This study has a lot of potential to handle difficult socioeconomic issues and offer vital information to academic, government, and analysts searching for fresh approaches in a variety of fieFSs.
Open Access: Yes
DOI: 10.3934/math.2025552
An insightful multicriteria model for the selection of drilling technique for heat extraction from geothermal reservoirs using a fuzzy-rough approach
Publication Name: Information Sciences
Publication Date: 2025-01-01
Volume: 686
Issue: Unknown
Page Range: Unknown
Description:
Geothermal energy stands out as an exceptional renewable resource for power generation, offering a consistent power production without the intermittency issues. Despite its potential to deliver a consistent supply of electricity on demand, geothermal adoption is hindered due to substantial costs. Utilising the most effective drilling method can alleviate this challenge by boosting efficiency and reducing operational costs. The primary goal of this study is to identify the best drilling method for extracting heat from geothermal reservoirs. This optimised approach facilitates better access to geothermal reservoirs, leading to increased heat recovery rates and improved project viability. Traditional methods often fall short in evaluating optimal drilling alternatives due to uncertainties. To address this, our research introduces an innovative paradigm that integrates novel T-Spherical Hesitant Fuzzy Rough (T−SHFR) set, method for the removal effects of criteria with a geometric mean and ranking alternatives with weights of criterion hybrid Multiple Criteria Decision-Making (MCDM) techniques. By leveraging the novel T−SHFR concept, our approach allows for a comprehensive assessment of various factors. This holistic evaluation ensures an exhaustive comprehension of the decision-making environment. The study reveals that reservoir characteristics play a significant role in selecting a sustainable drilling alternative. Furthermore, directional drilling appears as the most promising method with higher energy yields followed by slim hole drilling. The robustness and credibility of these findings are established through sensitivity and comparative analyses, indicating the potential applicability of this MCDM method to analogous challenges in different contexts. The findings of the ranking techniques were validated using Spearman's rank correlation coefficient, which revealed a positive and notable correlation. This research will empower stakeholders to make informed decisions, thereby enhancing the overall efficiency and sustainability of geothermal energy projects.
Open Access: Yes
Prioritization of AI-based material handling approaches for smart logistics in sustainable warehouses: A q-rung orthopair fuzzy CoCoSo methodology with consensus reaching
Publication Name: Environment Development and Sustainability
Publication Date: 2025-01-01
Volume: Unknown
Issue: Unknown
Page Range: Unknown
Description:
This study aims to address the artificial intelligence-based material handling approach selection problem under circular economy to contribute the smart and sustainable business management in logistics systems. The "consensus-reaching process" for experts is not emphasized in the current decision-making procedures with q-rung orthopair fuzzy data. Experts working on group decision-making challenges may hold views that are very dissimilar from one another as a result of their knowledge and experiences. In order for experts to increase the amount of consensus, a consensus-building process is needed. Besides, the ranking results provided by "combined compromise for ideal solution" do not change dramatically in line with the changing weight distributions of characteristics. So, q-rung orthopair fuzzy-based combined compromise for ideal solution methodology with consensus reaching is introduced for solving the addressed emerging problem of logistics companies. This robust and logical decision-making method can comprehensively analyze the advantages, disadvantages, and potential barriers to the acceptance of artificial intelligence-based material handling approaches. The real-life study is offered for a logistics company that plans to invest in robotic solutions based on artificial intelligence. The findings show that autonomous mobile robots represent the best artificial intelligence-based material handling approach. Recommendations for adopting alternative solutions are provided to assist in the efficient completion of smart logistics activities.
Open Access: Yes
New distance measures of complex Fermatean fuzzy sets with applications in decision making and clustering problems
Publication Name: Information Sciences
Publication Date: 2025-01-01
Volume: 686
Issue: Unknown
Page Range: Unknown
Description:
Complex Fermatean fuzzy sets (CFFSs) integrate the ideas of complex fuzzy sets and Fermatean fuzzy sets, where the membership, non-membership, and hesitancy degrees are all complex numbers, allowing the express uncertain information more flexibly and comprehensively. However, how to reasonably measure the discrepancies between CFFSs in decision-making remains an open task. This paper presents a series of new distance measures of CFFSs and their weighted versions based on Hamming, Euclidean, Hausdorff, and Hellinger distances. On this basis, we explore some outstanding properties that the proposed measures satisfy (i.e., boundedness, nondegeneracy, symmetry, and triangular inequality) and demonstrate their effectiveness through several examples. Furthermore, we design a decision-making algorithm as well as a clustering algorithm based on the proposed measures and verify the performance of the proposed measures through several applications.
Open Access: Yes
Application of Z-number based fuzzy MCDM in solar power plant location selection problem in Spatial planning
Publication Name: Energy Reports
Publication Date: 2024-12-01
Volume: 12
Issue: Unknown
Page Range: 4034-4054
Description:
In order to achieve sustainable energy consumption and development goals, it is of great importance to find suitable locations for the construction of solar power plants. In this study, Geographic Information System (GIS) and Z-Number iteration of Fuzzy Logarithm Additive Weights Methodology (F-LMAW), a recently adopted Multi-Criteria Decision Making Analysis (MCDA) technique, are used to identify the best locations for solar power plant construction in Mersin province. Nineteen criteria were selected for the study and their relative weights and usefulness in ranking the solar power plant locations were estimated. The Weighted Linear Combination (WLC) technique was used to determine the suitability index for solar power plant siting in the study area. According to the analysis made by taking into account the expert opinions for the site selection of solar power plants, the solar radiation criterion was the most important criterion with a weight value of 0,0664, while the distance from the river criterion was the least important criterion with a weight value of 0,0265. A potential suitability map for the solar power plant was produced with the suitability index values. According to the suitability index values, the study area exhibited suitability degrees for solar power plant siting ranging from “suitable (0,0038 %)” to “moderately suitable (0,0034 %)” and “very slightly suitable (0,0033 %)”. Silifke and Mut regions are considered as good locations for solar power plants in Mersin province. The robustness of the proposed technique was determined by sensitivity analysis.
Open Access: Yes
Enhancing decision-making with linear diophantine multi-fuzzy set: application of novel information measures in medical and engineering fields
Publication Name: Scientific Reports
Publication Date: 2024-12-01
Volume: 14
Issue: 1
Page Range: Unknown
Description:
This study offers a comprehensive analysis of novel information for linear diophantine multi-fuzzy sets and illustrates its applications in practical scenarios. We introduce innovative similarity metrics tailored for linear diophantine multi-fuzzy sets, including Cosine similarity, Jaccard similarity, and Exponential similarity. Additionally, we propose Entropy, Inclusion, and Distance measures, providing a robust theoretical foundation supported by developed theorems that explain the interactions between these metrics. The practical implications of these theoretical advancements are demonstrated through various case studies. Specifically, we apply the similarity measures to predict preeclampsia, a severe condition affecting pregnant women, showcasing their potential in medical diagnostics. The entropy measure is used to identify the optimal materials manufacturing method for medical surgical robots, underscoring its importance in ensuring patient safety and the effectiveness of medical procedures. Furthermore, the inclusion measure is employed in pattern recognition tasks, highlighting its utility in complex data analysis. The comparative and superiority analysis shows the effectiveness of our research. The novel aspect of this study is the implementation of information metrics for LDMFS. These efforts aim to enhance the impact and practical applicability of linear diophantine multi-fuzzy sets, fostering innovation and improving outcomes across multiple fields.
Open Access: Yes
Application of the new simple weight calculation (SIWEC) method in the case study in the sales channels of agricultural products
Publication Name: Methodsx
Publication Date: 2024-12-01
Volume: 13
Issue: Unknown
Page Range: Unknown
Description:
In this research is presented a new method for determining the weights of criteria called simple weight calculation (SIWEC) method. The steps of this method are presented in the practical example of determining the importance of criteria for the needs of sales of agricultural products in the Semberija region. During the presentation of this method two methods are elaborated the simple SIWEC method which includes numerical ratings and the fuzzy SIWEC method which includes ratings in the form of linguistic value. In the selected example is presented how to use this method in order to determine the importance of criteria and in both cases the criterion of sales reliability is given the greatest weight. The contribution SIWEC method is reflected in its simplicity, which facilitates decision-making. • The method presented in this research apart from others is that it uses the evaluation of the criteria by decision makers, so the criteria should not be ranked and compared, but simply evaluated. • Unlike similar methods, the presented method uses the adjusted steps of the method for ranking the alternatives, and decision makers are given a different importance in the decision-making.
Open Access: Yes
Technology adaptation in sugarcane supply chain based on a novel p, q Quasirung Orthopair Fuzzy decision making framework
Publication Name: Scientific Reports
Publication Date: 2024-12-01
Volume: 14
Issue: 1
Page Range: Unknown
Description:
The present paper contributes to the literature in two ways. First, it develops a novel p, q Quasirung Orthopair Fuzzy (p, q QOF) based group decision making framework to modify a recently developed multi-criteria decision making (MCDM) model such as Comparisons between Ranked Criteria (COBRAC). Second, the paper ruminates on the Strength-Weakness-Opportunity-Threat (SWOT) of the sugarcane supply chain (SSC) in India vis-à-vis adaptation of the advanced technologies featuring Industry 4.0. To set the sub-factors of various dimensions of SWOT, the theoretical ground of Technology-Organization-Environment (TOE) framework has been used. The sub-factors of SWOT have been derived through an informal in-depth discussion with the experts of the sugar industry. Then using a Likert five-point linguistic scale the experts rated the sub-factors based on their relative importance. To determine the weights the modified COBRAC method has been applied. In subsequent stages the reliability of the model has been tested and sensitivity analysis has been carried out to check the stability of the result. The analysis reveals that while experience, by-product utilization and high demand provides strength and create opportunities for SSC, the areas of concern are lack of variety, fragmented nature of supply chains, shortage of next-gen talent and inadequate infrastructure. However, there are enough promises for SSC. The paper shall provide impetus to strategic decision makers for the sugar industry and puts forth a new decision-making framework for the analysts.
Open Access: Yes
Cloud spot instance price forecasting multi-headed models tuned using modified PSO
Publication Name: Journal of King Saud University Science
Publication Date: 2024-12-01
Volume: 36
Issue: 11
Page Range: Unknown
Description:
The increasing dependence and demands on cloud infrastructure have brought to light challenges associated with cloud instance pricing. The often unpredictable nature of demand as well as changing costs of supplying a reliable instance can leave companies struggling to appropriately budget to support a healthy cash flow while maintaining operating costs. This work explores the potential of multi-headed recurrent architectures to forecast cloud instance prices based on historical and instance data. Two architectures are explored, long short-term memory (LSTM) and gated recurrent unit (GRU) networks. A modified optimizer is introduced and tested on a publicly available Amazon elastic compute cloud dataset. The GRU model, enhanced by the proposed modified approach, had the most impressive outcomes with an MAE score of 0.000801. Results have undergone meticulous statistical validation with the best-performing models further analyzed using explainable artificial intelligence techniques to provide further insight into model reasoning and information on feature importance.
Open Access: Yes
A Novel Evaluation Framework for Medical LLMs: Combining Fuzzy Logic and MCDM for Medical Relation and Clinical Concept Extraction
Publication Name: Journal of Medical Systems
Publication Date: 2024-12-01
Volume: 48
Issue: 1
Page Range: Unknown
Description:
Artificial intelligence (AI) has become a crucial element of modern technology, especially in the healthcare sector, which is apparent given the continuous development of large language models (LLMs), which are utilized in various domains, including medical beings. However, when it comes to using these LLMs for the medical domain, there’s a need for an evaluation platform to determine their suitability and drive future development efforts. Towards that end, this study aims to address this concern by developing a comprehensive Multi-Criteria Decision Making (MCDM) approach that is specifically designed to evaluate medical LLMs. The success of AI, particularly LLMs, in the healthcare domain, depends on their efficacy, safety, and ethical compliance. Therefore, it is essential to have a robust evaluation framework for their integration into medical contexts. This study proposes using the Fuzzy-Weighted Zero-InConsistency (FWZIC) method extended to p, q-quasirung orthopair fuzzy set (p, q-QROFS) for weighing evaluation criteria. This extension enables the handling of uncertainties inherent in medical decision-making processes. The approach accommodates the imprecise and multifaceted nature of real-world medical data and criteria by incorporating fuzzy logic principles. The MultiAtributive Ideal-Real Comparative Analysis (MAIRCA) method is employed for the assessment of medical LLMs utilized in the case study of this research. The results of this research revealed that “Medical Relation Extraction” criteria with its sub-levels had more importance with (0.504) than “Clinical Concept Extraction” with (0.495). For the LLMs evaluated, out of 6 alternatives, (A4) “GatorTron S 10B” had the 1st rank as compared to (A1) “GatorTron 90B” had the 6th rank. The implications of this study extend beyond academic discourse, directly impacting healthcare practices and patient outcomes. The proposed framework can help healthcare professionals make more informed decisions regarding the adoption and utilization of LLMs in medical settings.
Open Access: Yes
Artificial intelligence-based expert weighted quantum picture fuzzy rough sets and recommendation system for metaverse investment decision-making priorities
Publication Name: Artificial Intelligence Review
Publication Date: 2024-10-01
Volume: 57
Issue: 10
Page Range: Unknown
Description:
There should be some improvements to increase the performance of Metaverse investments. However, businesses need to focus on the most important actions to provide cost effectiveness in this process. In summary, a new study is needed in which a priority analysis is made for the performance indicators of Metaverse investments. Accordingly, this study aims to evaluate the main determinants of the performance of the metaverse investments. Within this context, a novel model is created that has four different stages. The first stage is related to the prioritizing the experts with artificial intelligence-based decision-making method. Secondly, missing evaluations are estimated by expert recommendation system. Thirdly, the criteria are weighted with Quantum picture fuzzy rough sets-based (QPFR) M-Step-wise Weight Assessment Ratio Analysis (SWARA). Finally, investment decision-making priorities are ranked by QPFR VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje). The main contribution of this study is the integration of the artificial intelligence methodology to the fuzzy decision-making approach for the purpose of computing the weights of the decision makers. Owing to this condition, the evaluations of these people are examined according to their qualifications. This situation has a positive contribution to make more effective evaluations. Organizational effectiveness is found to be the most important factor in improving the performance of metaverse investments. Similarly, it is also identified that it is important for businesses to ensure technological improvements in the development of Metaverse investments. On the other side, the ranking results indicate that regulatory framework is the most critical alternative in this regard.
Open Access: Yes
Novel α-divergence measures on picture fuzzy sets and interval-valued picture fuzzy sets with diverse applications
Publication Name: Engineering Applications of Artificial Intelligence
Publication Date: 2024-10-01
Volume: 136
Issue: Unknown
Page Range: Unknown
Description:
Currently, many studies have developed distance or divergence measures between intuitionistic fuzzy sets (IFSs) and interval-valued fuzzy sets (IvFSs). As a generalization of IFSs, picture fuzzy sets (PFSs) provide a more nuanced representation of uncertain and ambiguous information. Interval-valued picture fuzzy sets (IvPFSs) combine the concepts of IvIFSs and PFSs, providing a highly effective means of representing and processing uncertain, ambiguous and incomplete information. How to better measure the differences between PFSs and IvPFSs is still an open issue. This paper proposes some novel α-divergence measures for PFSs and IvPFSs, respectively. We demonstrate the basic properties of the proposed divergence measures, including non-negativity, non-degeneracy and symmetry. Besides, we analyze some special cases of the proposed divergence measures that degenerate into or are related to several well-known divergences. Then, we construct some numerical examples to demonstrate the effectiveness of the proposed measures concerning existing measures. Finally, the proposed α-divergence measures are applied to pattern recognition, multi-attribute decision-making (MADM) and clustering, demonstrating that these measures possess a high confidence level and can produce trustworthy results, especially in comparable situations.
Open Access: Yes
SELECTION OF AGRICULTURAL PRODUCT SALES CHANNELS USING FUZZY DOUBLE MEREC AND FUZZY RAWEC METHOD
Publication Name: Agriculture and Forestry
Publication Date: 2024-09-30
Volume: 70
Issue: 3
Page Range: 45-58
Description:
When selling food products, it's important to choose the appropriate sales channel. These channels connect producers with consumers. The aim of this study was to select a channel for the sale of cabbage to end customers. In this paper, six different sales channels that are used in the Semberija region for the sale of cabbage were observed. These sales channels were evaluated using 11 different criteria. In order to choose the sales channel that best meets the set objectives, a fuzzy set approach was used. This approach was chosen because qualitative criteria were used and expert ratings were in the form of linguistic values. Based on the input of seven experts who are professors at agricultural faculties in Bijeljina, it was found that consumer habits were the most important criterion, followed by the criterion compliance with environmental standards, while the smallest weight value was given to the criterion delivery method. Using the RAWEC (Ranking of Alternatives with Weights of Criterion) method, it was shown that online sales yield the best results, after that follows Producer-sales agent-consumer, while according to experts, the sales channel is the best rated Producer-wholesaler-retailer-consumer. This is because various tools can be utilized on the Internet for selling agricultural products. Based on the conducted research, the contribution of this study lies in the selection of sales channels using the integration of the MEREC and RAWEC methods.
Open Access: Yes
Analysis of Hamacher power aggregation operators for circular complex p, q-quasirung orthopair fuzzy 2-tuple linguistic sets and their application in green industry development
Publication Name: Heliyon
Publication Date: 2024-09-15
Volume: 10
Issue: 17
Page Range: Unknown
Description:
Green industry development focuses on balancing economic and financial growth with environmental stewardship and ensuring that companies and industries are contributing positively to both environmental sustainability and prosperity. This manuscript aims to develop the novel technique of circular complex p, q-quasirung orthopair fuzzy 2-tuple linguistic (CCp, q-QOF2-TL) set and their operational laws based on algebraic t-norms and Hamacher t-norms, where the algebraic t-norms and Einstein t-norms are the special cases of the Hamacher t-norms for parameter ϜℲs=l and ϜℲs=2. Further, we derive the Hamacher power aggregation operators based on any finite collection of CCp, q-QOF2-TL numbers (CCp, q-QOF2-TLNs), called CCp, q-QOF2-TL Hamacher power average (CCp, q-QOF2-TLHPA) operator, CCp, q-QOF2-TL Hamacher power weighted average (CCp, q-QOF2-TLHPWA) operator, CCp, q-QOF2-TL Hamacher power geometric (CCp, q-QOF2-TLHPG) operator, CCp, q-QOF2-TL Hamacher power weighted geometric (CCp, q-QOF2-TLHPWG) operator, and described their basic properties, called idempotency, monotonicity, and boundedness. Further, we demonstrate the technique of multi-attribute decision-making (MADM) problem based on the above operators to evaluate the major factor that will be playing in the development of the green industry. Finally, we compare the proposed ranking values with the obtained ranking values of existing techniques to show the supremacy and superiority of the initiated approaches.
Open Access: Yes
CONVERGENCE STRATEGIES FOR OPTIMIZING ANTENNA SELECTION IN A COMMUNICATION SYSTEM: A COMPLEX LINEAR DIOPHANTINE FUZZY SOFT SET APPROACH
Publication Name: Applied Engineering Letters
Publication Date: 2024-09-01
Volume: 9
Issue: 3
Page Range: 146-161
Description:
The need to grow in a secure and tranquil environment demands the efforts of an armed force, and only with a strong-armed force can a country ensure its national security. In military activities, communication devices are widely used to confuse enemies' radars or communications to abandon their strategies and execute planned actions. The range of communication devices depends mainly on the antennas used. Army sustainability goals are to upgrade the effectiveness of the mission, reduce army environmental impact, build green sustainable structures, and attain the energy level independence that improves the continuity of operations which are indispensable to the mission. The primary goal of this paper is to present an innovative mathematical model for selecting pertinent antennae in communication devices using an innovative idea called a Complex Linear Diophantine Fuzzy Soft set based on the various attributes by incorporating decision-making techniques. Also, some of its beneficial operations such as Complement, AND, OR, Extended Union, and Extended Intersection, are presented in concert with the properties and theorems to apprise the viability of the proposed paper. This concept is more applicable and necessary to assess real-life situations using mathematical modeling.
Open Access: Yes
Analysis of coupling in geographic information systems based on WASPAS method for bipolar complex fuzzy linguistic Aczel-Alsina power aggregation operators
Publication Name: Plos One
Publication Date: 2024-09-01
Volume: 19
Issue: 9
Page Range: Unknown
Description:
The model of bipolar complex fuzzy linguistic set is a very famous and dominant principle to cope with vague and uncertain information. The bipolar complex fuzzy linguistic set contained the positive membership function, negative membership function, and linguistic variable, where the technique of fuzzy sets to bipolar fuzzy sets are the special cases of the bipolar complex fuzzy linguistic set. In this manuscript, we describe the model of Aczel-Alsina operational laws for bipolar complex fuzzy linguistic values based on Aczel-Alsina t-norm and Aczel-Alsina t-conorm. Additionally, we compute the Aczel-Alsina power aggregation operators based on bipolar complex fuzzy linguistic data, called bipolar complex fuzzy linguistic Aczel-Alsina power averaging operator, bipolar complex fuzzy linguistic Aczel-Alsina power weighted averaging operator, bipolar complex fuzzy linguistic Aczel-Alsina power geometric operator, and bipolar complex fuzzy linguistic Aczel-Alsina power weighted geometric operator with some dominant and fundamental laws such as idempotency, monotonicity, and boundedness. Moreover, we initiate the model of the Weighted Aggregates Sum Product Assessment technique with the help of consequent theory. In the context of geographic information systems and spatial information systems, coupling aims to find out the relationships among different components within a geographic information system, where coupling can occur at many stages, for instance, spatial coupling, data coupling, and functional coupling. To evaluate the above dilemma, we perform the model of multi-attribute decision-making for invented operators to compute the best technique for addressing geographic information systems. In the last, we deliberate some numerical examples for comparing the ranking results of proposed and prevailing techniques.
Open Access: Yes
Holistic evaluation of energy transition technology investments using an integrated recommender system and artificial intelligence-based fuzzy decision-making approach
Publication Name: Results in Engineering
Publication Date: 2024-09-01
Volume: 23
Issue: Unknown
Page Range: Unknown
Description:
The most essential criteria should be determined in the selection of the suitable energy transition technologies due to budget deficit problem. Therefore, it is necessary to identify the most important criteria in energy transition technology selection. Therefore, a new study is needed to determine the most prominent issues in the correct selection of energy transition technologies. The purpose of this study is to identify the most appropriate energy transition technology alternative. Within this framework, a novel artificial intelligence (AI)-based fuzzy decision-making model has been presented. In the first part, the experts are prioritized by the help of AI methodology. In the next section, missing evaluations of energy transition technology investments are estimated via expert recommender system. Thirdly, the weights of the criteria for energy transition technology selection are computed by quantum picture fuzzy rough sets (QPFR) M-Stepwise Weight Assessment Ratio Analysis (SWARA). At the final stage, selected energy transition technology alternatives are ranked via QPFR-Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR). The main contribution of this study is the integration of AI technique to the proposed model. Similar to this issue, using M-SWARA methodology in the process of criteria weighting increases the quality of the findings. This methodology helps to consider the impact relation map of the criteria. The findings demonstrate that the most important factor is cost-effectiveness of energy transition. Similarly, it is also found that the local ecosystem is the second most significant issue. On the other side, the ranking results denote that compact renewable systems for small scale production is the most optimal solution of energy transition technology alternatives.
Open Access: Yes
"Thin" Structure of Relations in MCDM Models. Equivalence of the MABAC, TOPSIS(L1) and RS Methods to the Weighted Sum Method
Publication Name: Decision Making Applications in Management and Engineering
Publication Date: 2024-01-23
Volume: 7
Issue: 2
Page Range: 418-442
Description:
This paper introduces the conceptual framework of the multi-criteria decision-making (MCDM) rank model, which embodies the integration and harmonization of the aggregation method, the weighing method, the decision matrix normalization technique, and the selection of distance metrics. This definition serves to broaden the spectrum of acceptable MCDM methodologies for problem-solving and specifiing the associated tools. A Multi-Method Model (3M) approach is employed for multi-criteria selection to enhance the reliability of the results. The methodology is outlined for adjusting the rankings of alternatives to account for the distinguishability of ratings in a particular MCDM model using the Relative Performance Indicator (RPI) of alternatives. Through RPI, four methods are established for aggregating individual characteristics of alternatives that yield identical results: Weighted Sum Model (WSM), Multi-Attributive Border Approximation area Comparison (MABAC), Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS (L1)), and Ratio System approach (RS), eliminating the need to duplicate these methods in the 3M approach. A comprehensive comparison of numerous multi-criteria methods is conducted based on two lists: ranking and rating. Additionally, a method for step-by-step linear transformation of alternative ratings obtained from various MCDM models is defined, facilitating comparison and aggregation of ratings.
Open Access: Yes
Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) Method: A Comprehensive Bibliometric Analysis
Publication Name: Decision Making Applications in Management and Engineering
Publication Date: 2024-01-23
Volume: 7
Issue: 2
Page Range: 313-336
Description:
This paper explores the evolution, applications, and prospective developments of a very popular multi-criteria decision-making (MCDM) method called Measurement of Alternatives and Ranking according to COmpromise Solution Method (MARCOS). Employing an extensive bibliometric analysis, the study examines 115 pertinent papers sourced from the Scopus database spanning over the years from 2020 to 2024. This study also provides an evaluation of the methodological significance and outlines potential future directions of MARCOS method. The outcomes indicate "Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS)" by Stević et al. (2020) as the most cited paper. Journals such as "Sustainability (Switzerland)", "Mathematics" and "Expert Systems with Applications" stand out among the most cited journals. "University of East Sarajevo" is an institution distinguished for its prolific research in this field. "Stević Ž." Has been identified as the most cited and published author. The most frequently used keywords are "MARCOS", "MARCOS method", and "MCDM". CRiteria Importance Through Intercriteria. Correlation (CRITIC) method is a weighting model often integrated with MARCOS method. The results of the study provide researchers and practitioners in the field of MCDM with an important insight into the current state of the MARCOS methodology, highlighted studies and potential future developments. It also provides a comprehensive overview of the importance of this method in the multi-criteria decision-making literature, shedding light on future research directions.
Open Access: Yes
Application of the FUCOM-FUZZY MAIRCA Model in Human Resource Management
Publication Name: Acta Polytechnica Hungarica
Publication Date: 2023-01-01
Volume: 20
Issue: 3
Page Range: 231-249
Description:
The paper presents the FUCOM-FMAIRCA MCDM model for application in human resource management. The proposed model allows the inclusion of all relevant stakeholders in the process of human resource selection, enhances the pool of scientific knowledge in the field of human resource management highlighting selection as a special activity, and uses modern quantitative (mathematical) decision-making methods. Based on the analysis of personality traits of teachers and literature related to this field, the necessary characteristics of teachers of the Military Academy are presented, on the basis of which the selection criteria are formed. The FUCOM method was used to define the weight coefficients of the defined criteria. In order to more precisely determine the qualitative properties and their quantification, triangular fuzzy numbers were implemented in the MAIRCA method, and by applying all the steps of this method, the ranking of alternatives was performed. Finally, in order to test the validity of the model, a sensitivity analysis was carried out.
Open Access: Yes
Risk Management for Cold Supply Chain: Case of a Developing Country
Publication Name: Acta Polytechnica Hungarica
Publication Date: 2022-01-01
Volume: 19
Issue: 8
Page Range: 161-185
Description:
Cold Supply Chain (CSC) involves temperature-controlled activities in the overall process, ranging from the raw material storage to the final supply of the products to the consumers. The activities involved are easily exposed to risks such as temperature and humidity, equipment failure and quality risk to name a few. Such sensitive processes need proper risk mitigation strategies, to ensure the effective functioning of the overall CSC. For this purpose, the current research conducted a vigorous literature review and identified 40 relevant risks related to CSC in a developing country. The risks were analyzed using Failure Mode and Effect Analysis (FMEA)-Risk Priority Number (RPN) technique to shortlist the significant risks. The significant risks were then subjected to the Full Consistency Method (FUCOM) for prioritization. The results concluded, contamination of food, temperature and humidity and quality as the top-three risks that can be dangerous for the overall cold supply chain. To overcome these risks, the study recommends the proper implementation of traceability systems and Radio Frequency Identification (RFID) systems. Furthermore, employing the latest technologies and efficient personnel training can also help overcome these risks. Such an application of the study in the case of a developing country, Pakistan's CSC forms to be the first of its kind. Furthermore, the application of FMEA-RPN along with the FUCOM technique in the scenario of CSC risk management forms the novelty of this research study.
Open Access: Yes
DOI: DOI not available
Nonlocal complex short pulse equation in -symmetry like symmetry breaking, breather–grammian interactions and soliton solutions
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
Research on -symmetry and spontaneous symmetry breaking captivates contemporary scholars due to its extensive applicability in several fields, including microwave propagation and nonlinear optics. This article studies the nonlocal complex short pulse (NL-CSP) equation in which we discuss how under certain symmetry reduction general complex short pulse equation turns into NL-CSP equation. We construct the binary Darboux transformation for the reverse space-time NL-CSP equation and derive its quasi-grammian solutions. Further, we obtain explicit expressions for spontaneous symmetry-breaking and symmetry-preserving breather, interaction of breather with grammian and also the soliton solutions. It is concluded that the existence of both symmetry-breaking and symmetry-preserving solutions for NL-CSP equation. Finally, to verify the theoretical results, we illustrate the dynamics of these solutions using surface and contour plots.
Open Access: Yes
Biogeography-Based Optimization of Machine Learning Models for Accurate Penetration Rate Prediction Using Rock Texture Coefficient
Publication Name: International Journal of Computational Intelligence Systems
Publication Date: 2025-12-01
Volume: 18
Issue: 1
Page Range: Unknown
Description:
Predicting drill penetration rate (PR) in rock environments remains a significant challenge due to the complex interplay between rock texture, drilling fluid properties, and operational parameters. Traditional empirical models often lack generalizability and are based on inconsistent datasets, limiting their reliability. To address these limitations, this study develops a comprehensive experimental dataset using rock samples collected from various mines in Iran, tested under controlled laboratory conditions with different drilling fluids, bit loads, and rotational speeds. Texture coefficient (TC), electrical conductivity (EC), load on bit (LOB), and bit rotational velocity (BRV) were selected as input features. Four machine learning models—support vector regression (SVR), stochastic gradient descent (SGD), K-nearest neighbors (KNN), and decision tree (DT)—were trained to predict PR. A biogeography-based optimization (BBO) algorithm was employed to fine-tune hyperparameters and enhance model accuracy. Additionally, a novel hybrid error index (HEI) was introduced to comprehensively evaluate model performance. Among all models, the DT achieved the best accuracy with an HEI of 0.3753, followed by KNN, SVR, and SGD. These findings demonstrate the potential of the DT model, combined with optimized learning and a robust dataset, to reliably predict penetration rate in rock-based engineering projects.
Open Access: Yes
A hybrid physics-informed neural and explainable AI approach for scalable and interpretable AQI predictions
Publication Name: Methodsx
Publication Date: 2025-12-01
Volume: 15
Issue: Unknown
Page Range: Unknown
Description:
Air Pollution is a critical environmental issue affecting public health, climate, and ecosystems. However, accurately predicting and classifying Air Quality Index (AQI) levels across different regions remains a challenging task due to the complex nature of air pollution patterns. Conventional and ensemble ML and DL models often fail to capture the physical laws goverming the air pollution, which leads to inaccurate predictions. This study addresses these issues by introducing an approach that employs Physics-Informed Neural Networks (PINN) with Explainable AI (XAI) techniques for AQI classification (AirSense-X). The proposed approach utilizes PINN for regression, along with mapping for classification and XAI for interpretation. PINN ensures that the model learns from physical laws governing air quality rather than relying solely on data. The dataset utilized in this study is a publicly available dataset containing the AQI data at daily levels from various stations across multiple cities in India. The proposed AirSense-X approach achieves an accuracy of 98 %, with 97 % precision, 95 % recall, and an F1 score of 0.96, ensuring reliability. Similarly, the confusion matrix for the proposed approach indicated that the model correctly classified 21,306 and misclassified 268 instances. The key focuses of this study include: • Introducing a novel approach, AirSense-X, which employs PINN for accurate AQI prediction and XAI for enhanced interpretability. Additionally, the study also involves comparative analysis with conventional and ensemble ML and DL models. • Employing structure mapping technique for classification based on the predicted AQI values. • Integrating physical laws governing air pollution using a PINN model enhances prediction accuracy and ensures that the model learns beyond relying on data-driven insights.
Open Access: Yes
Reliable generative interpretable framework for efficient predictive analysis of air quality index
Publication Name: Egyptian Informatics Journal
Publication Date: 2025-09-01
Volume: 31
Issue: Unknown
Page Range: Unknown
Description:
Air quality management is one of the most important sustainability goals in the era of Industry 5.0. The magnitude of air pollution and impact of drastic pollutants increase day by day despite the significant efforts of the environmental enthusiasts and researchers. The role of Artificial Intelligence (AI) in determining the Air Quality Index (AQI) is significant with reasonable accuracy of classification achieved. The proposed model is a multi-class problem, that classifies the AQI into six different classes. Various ML models such as Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting(GB), Logistic Regression (LR). The RF provided reliable performance metrics for AQI category prediction, achieving an accuracy and Precision of 0.99. This model is selected for the implementation of Explainable AI (XAI) models such as Local Interpretable Model Agonistic Explainer (LIME) for explanation using the local surrogacy plots and SHapley Additive exPlanations (SHAP) explainer for the global surrogacy plots. The Generative Adversarial Network (GAN) can generate synthetic data, which addresses critical issues such as missing data, class imbalance, noise, and redundant data. The performance the GAN shows optimized performance in classification of the AQI data with accuracy closer to 100 %. This is mainly due to the synthetic data generated by the GAN which enhances the performance of the classification. The proposed work integrates the efforts of the GAN-AI-XAI that enhances the performance, reliability, trustworthiness and robustness of the AQI classification model.
Open Access: Yes
Computational Assessment of Energy Supply Sustainability Using Picture Fuzzy Choquet Integral Decision Support System
Publication Name: Computers Materials and Continua
Publication Date: 2025-01-01
Volume: 85
Issue: 1
Page Range: 1311-1337
Description:
For any country, the availability of electricity is crucial to the development of the national economy and society. As a result, decision-makers and policy-makers can improve the sustainability and security of the energy supply by implementing a variety of actions by using the evaluation of these factors as an early warning system. This research aims to provide a multi-criterion decision-making (MCDM) method for assessing the sustainability and security of the electrical supply. The weights of criteria, which indicate their relative relevance in the assessment of the sustainability and security of the energy supply, the MCDM method allow users to express their opinions. To overcome the impact of uncertainty and vagueness of expert opinion, we explore the notion of picture fuzzy theory, which is a more efficient and dominant mathematical model. Recently, the theory of Aczel-Alsina operations has attained a lot of attraction and has an extensive capability to acquire smooth approximated results during the aggregation process. However, Choquet integral operators are more flexible and are used to express correlation among different attributes. This article diagnoses an innovative theory of picture fuzzy set to derive robust mathematical methodologies of picture fuzzy Choquet Integral Aczel-Alsina aggregation operators. To prove the intensity and validity of invented approaches, some dominant properties and special cases are also discussed. An intelligent decision algorithm for the MCDM problem is designed to resolve complicated real-life applications under multiple conflicting criteria. Additionally, we discussed a numerical example to investigate a suitable electric transformer under consideration of different beneficial key criteria. A comparative study is established to capture the superiority and effectiveness of pioneered mathematical approaches with existing methodologies.
Open Access: Yes
Adaptive few-shot tiny neural systems for real-time traffic intensity prediction in smart cities
Publication Name: ICT Express
Publication Date: 2025-01-01
Volume: Unknown
Issue: Unknown
Page Range: Unknown
Description:
The rapid evolution of urban mobility and smart city demands an intelligent transportation system which can make real-time decisions using lightweight and adaptive AI models. This research introduces a novel application of tiny machine learning which will combine the features of Few-shot learning algorithm and it will classify the traffic intensity levels on regional traffic data. By converting the traffic volume into three dynamic classes (Low/ Medium/ High), a compact neural network model is trained on episodic few-shot tasks that can mimic real-world low-data learning conditions. The proposed work supports open set classification which is more suitable for detecting unknown traffic behavior analysis by considering the previous day traffic level and how the future traffic intensity level can be predicted effectively. The accuracy of the proposed method is compared with the existing methods which lie with the baseline CNN (90 %) and SVM (89 %). But the average episode accuracy achieved through the proposed model is 95.2 % which makes this model promising for low-power edge deployment in intelligent transportation system.
Open Access: Yes
Optimizing industrial robot selection using novel trigonometric Pythagorean fuzzy normal aggregation operators
Publication Name: Complex and Intelligent Systems
Publication Date: 2025-10-01
Volume: 11
Issue: 10
Page Range: Unknown
Description:
The modern world uses an increasing number of robots, notably service robots. Robots will be able to easily manipulate everyday objects in the future, but only if they are paired with planning and decision-making procedures that allow them to comprehend how to complete a task. This research presents new techniques to handling multi-attribute problem solving with trigonometric Pythagorean normal fuzzy numbers. The sine trigonometric Pythagorean fuzzy sets combine the concept of Pythagorean fuzzy sets with sine trigonometric functions to represent uncertainty in decision-making. It is feasible to combine trigonometric Pythagorean fuzzy numbers and normal fuzzy numbers to get trigonometric Pythagorean fuzzy normal numbers. In addition to the fundamental interaction aggregation operators, we define the trigonometric Pythagorean fuzzy normal numbers. The trigonometric Pythagorean fuzzy normal numbers satisfy the following properties: associative, distributive, idempotent, bounded, commutative and monotonicity. Four novel approaches are introduced such as weighted averaging, weighted geometric, generalized weighted averaging and generalized weighted geometric. These operators can be used in the development of a multi-attribute decision-making algorithm. We demonstrate how improved Euclidean and Hamming distances are used in practical situations. For industrial robots, the two most crucial elements are computer science and machine tool technology. The four criteria of weights, orientations, speeds and accuracy may be used to assess robotic systems. They are also more practical, easier to understand, and more adept at identifying the best answer more quickly. The effectiveness and accuracy of the models we are looking at are demonstrated by comparing many existing models with those that have been developed.
Open Access: Yes
Food safety risk analysis utilising K-lexicographic-max product of neutrosophic graph
Publication Name: Ain Shams Engineering Journal
Publication Date: 2025-12-01
Volume: 16
Issue: 12
Page Range: Unknown
Description:
In this study, we introduce the concept of the K-Lexicographic Max Product (K−LMP) of neutrosophic graphs and explore its associated degree structure to enhance decision-making frameworks in food safety applications related to risk assessment, including freshness, contamination, and spoilage. Neutrosophic graphs, capable of handling indeterminacy, inconsistency, and incompleteness, provide a flexible mathematical foundation for modelling complex systems. By incorporating the K−LMP into neutrosophic graphs, we offer a novel approach to comparing and ranking food safety scenarios where multiple attributes and uncertain information coexist. We present example graphs and theorems related to K−LMP and further define the K-Lexicographic degree to quantify node significance within the context of neutrosophic graphs. To validate the practical utility of this approach, a food safety analysis is implemented, demonstrating how the model identifies critical control points and supports more robust, transparent decision-making under uncertainty. This work contributes to the advancement of neutrosophic graph theory and its interdisciplinary application in food quality and safety management.
Open Access: Yes
Equivalence of MCDM Methods and Synthesis of Solution Based on Ratings Obtained in Different Models
Publication Name: Decision Making Applications in Management and Engineering
Publication Date: 2025-01-01
Volume: 8
Issue: 2
Page Range: 1-20
Description:
Synthesis of solutions based on a set of models is a modern trend in the field of multi-criteria choice. It is assumed that a solution based on many methods increases the reliability of the decisions made. One of the important tasks is to select an independent set of models. Comparison of various multi-criteria methods is performed using two lists: rank and rating. To compare the rating of alternatives obtained using different MCDM models, the article uses the Relative Performance Indicator (RPI). Using RPI, six identical methods for aggregating private attributes of alternatives are established: Weighted Sum Model (WSM), Ratio System approach (RS), Multi-Attributive Border Approximation area Comparison (MABAC), Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) with L1 metric, Multi Atributive Ideal-Real Comparative Analysis (MAIRCA) and Ranking of Alternatives with Weights of Criterion (RAWEC) provided that each aggregation method combines the same method of linear normalization of attributes. This allows avoiding duplication of equivalent methods in the Multi-Method Model (3M) approach combining different MCDM models. When solving MCDM problems, it is recommended to use the simplest and most easily interpreted of them: WSM. The presented methodology is recommended as mandatory for the analysis of new or hybrid MCDM methods to eliminate duplication of existing methods. A synthesis of a solution based on ratings obtained in different MCDM models within the 3M approach is proposed. The method includes coordinating the common goal of several models and bringing the ratings obtained in different MCDM models to a common scale, which allows comparing and aggregating the ratings. The resulting rating is more informative than a rating based on ranks, such as Borda rules or similar, since it reflects the real proportions of the effectiveness of alternatives in different models.
Open Access: Yes
A Decision Framework for Course Recommendation Using Basic Uncertain Linguistic Information Soft Sets
Publication Name: Decision Making Applications in Management and Engineering
Publication Date: 2025-01-01
Volume: 8
Issue: 2
Page Range: 165-184
Description:
The aim of this paper is to provide fundamental theoretical studies on basic uncertain linguistic information soft set (BULISS). Firstly, the combination of basic uncertain linguistic information and soft set is introduced. Next, set operations and similarity measure on basic uncertain linguistic information soft sets and their properties are discussed. A novel application of basic uncertain linguistic information soft set to multi-criteria group decision making is put forward, in which the similarity measure between any two BULISSs is developed. A group decision algorithm by utilizing traditional decision procedure of soft set theory (or fuzzy soft set theory) and optimization method is given. Finally, a case study relating to curriculum recommendation is shown to illustrate feasibility and validity of the developed group decision making approach.
Open Access: Yes
Analysis of Wireless Communications for Smart Grid: MABAC Model Based on Complex Propositional Picture Fuzzy Sugeno Weber Power Aggregation Information
Publication Name: Systems and Soft Computing
Publication Date: 2025-12-01
Volume: 7
Issue: Unknown
Page Range: Unknown
Description:
In this study, the shortcoming of the conventional procedure is demonstrated by proposing the novel technique of complex propositional picture fuzzy sets with some fundamental concepts based on algebraic and Sugeno Weber norms. In addition, the authors classified the different types of power operators based on Sugeno Weber norms for complex propositional picture fuzzy values, called the complex propositional picture fuzzy Sugeno Weber power averaging, complex propositional picture fuzzy Sugeno Weber weighted power averaging, complex propositional picture fuzzy Sugeno Weber power geometric, complex propositional picture fuzzy Sugeno Weber weighted power geometric operators and also designed their three different properties for each operator. As well, the authors designed the multi-attributive border approximation area comparison for the proposed operator. Further, wireless communication networks are playing a critical and vital role in the circumstance of development and operation of smart grids, which incorporate advanced technologies to enhance the capability, efficiency, and sustainability of electricity distribution. Finally, the designed techniques and models are applied to the wireless communications for smart grids in Taiwan. Sensitivity and comparative analysis are derived to obey the strength and competence of the developed model. This study gives an inventive decision analysis structure, which varieties a substantial contribution to wireless communication in smart grid assessment difficulties under the indeterminate situation.
Open Access: Yes
A Human-Aided Evaluation Based on Distance from Average Solution Method for the Diagnosis of Skin Disease Using T-Spherical Fuzzy Information
Publication Name: Contemporary Mathematics Singapore
Publication Date: 2025-01-01
Volume: 6
Issue: 5
Page Range: 6689-6713
Description:
Disorders of the skin have been identified as skin diseases. These medical disorders may involve severe skin manifestations, including allergic reactions, frustration, and itching. Numerous skin disorders may be inherited, while other aspects may be caused by lifestyle. To diagnose the various skin disorders based on the symptoms of skin diseases, we introduce the novel idea of Interval-Valued T-Spherical Fuzzy Set (IV-TSFS) that significantly enhances the ability to handle vagueness and unpredictability in the data being gathered. The IV-TSFS takes the concept of T-SFS by incorporating Interval Values (IVs). This innovation greatly improves the capacity to represent and manage uncertainty because they offer a structured and flexible framework that captures real-world ambiguity, vagueness, and unpredictability as compared to other classical fuzzy models. In this article, we construct the extended conventional IV-TSF Evaluation based on Distance from Average Solution (EDAS) approach by using the conventional Evaluation based on Distance from Average Solution (EDAS) method and also identifying a wide range of possibilities and understanding the potential variability in outcomes, which is especially useful in Decision-Making (DM) scenarios. This method provides a balanced view of each alternative’s performance, helping decision-makers to rank and select the most suitable option effectively. It is the most powerful way to visualize and compare the performance of various alternatives in a structured and quantitative manner. Firstly, we briefly review the description of T-SFSs and IV-TSFSs and discuss the score function Ṩcr(₮), accuracy function Ἇcr(₮), and the basic Operational Laws (OLs) of IV-TSFVs. Next, we explain the extensive interventions of the extended conventional Interval-Valued T-Spherical Fuzzy (IV-TSF) EDAS method to cope with uncertain and unreliable information, which is especially useful in DM scenarios. Finally, a numerical example is provided to effectively diagnose the favorable skin disease based on the symptoms of skin diseases by using the IV-TSF EDAS approach, and several comparative results of our proposed model with other existing Aggregation Operators (AOs) are carried out to demonstrate the invaluable benefits associated with this strategy.
Open Access: Yes
Coherent control of reflection and transmission solitons of structured light via a gain-assisted medium
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
A gain-assisted atomic medium controls and modifies spatial solitons of reflection and transmission of structured light. Structured light pulses of reflection and transmission are generated and analyzed by azimuthal quantum numbers dependent on control driving fields in the medium. The study revealed the formation of spatial bright and dark solitons. The bright and dark soliton splitting regions are linearly increasing according to azimuthal quantum numbers of formula. Two, four, six, and eight bright and dark soliton regions are investigated with the azimuthal quantum number of. The structured light of the reflection pulse maintained a constant shape, exhibiting weak nonlinearity along the x-axis and strong nonlinearity along the y-axis. However, the structured light transmission pulse displayed varying shapes, influenced by the balanced nonlinearities along both the x- and y-axes at higher azimuthal quantum number, leading to stable propagation of spatial bright solitons. These findings highlight the significant role of the structured light effect in controlling and stabilizing soliton dynamics, with potential applications in nonlinear optics, traffic flow, signal processing, plasma physics, quantum field theory, and optical soliton interferometry.
Open Access: Yes
Energy Storage System Selection by Using Complex Intuitionistic Fuzzy Rough MCDM Technique Based on Schweizer-Sklar Operators
Publication Name: Contemporary Mathematics Singapore
Publication Date: 2025-01-01
Volume: 6
Issue: 5
Page Range: 7011-7040
Description:
Energy Storage System (ESS) is a talented solution to overcome the intermittency (that they do not produce energy all the time) and demand-supply misalliance problems in different renewable energy systems. Selecting the most optimal ESS requires the consideration of different conflicting criteria under uncertainty. This study presents a novel Multi-Criteria Decision-Making (MCDM) framework based on Complex Intuitionistic Fuzzy Rough Sets (CIFRSs) and Schweizer-Sklar aggregation operators to facilitate a more comprehensive and flexible ESS selection process. Specifically, we develop new aggregation operators namely, the Complex Intuitionistic Fuzzy Rough (CIFR) Schweizer-Sklar weighted average and the CIFR Schweizer-Sklar weighted geometric operators to model imprecise, vague, and inconsistent information. CIFR-MCDM methodology captures the intuitionistic, roughness and extra related fuzzy information in one structure. A case study is performed to illustrate the applicability of the suggested method in ranking different ESS alternatives. Comparative analysis with existing approaches confirms the robustness and effectiveness of the proposed framework in handling complex decision environments. The results highlight the potential of the CIFR-MCDM methodology to support informed and reliable ESS selection in renewable energy applications.
Open Access: Yes
Reducing Train Delays with Machine Learning-Based Predictive Maintenance for Railways
Publication Name: Decision Making Applications in Management and Engineering
Publication Date: 2025-01-01
Volume: 8
Issue: 2
Page Range: 265-284
Description:
The railway network constitutes a vital component of public transportation in many countries, serving millions of passengers and transporting significant volumes of freight. Nevertheless, a persistent challenge within this system is the frequent occurrence of train delays, which arise from diverse causes and result in financial losses, passenger dissatisfaction, and diminished trust among users. Consequently, enhancing operational efficiency and minimising delays has become a central objective for transportation planners and policymakers. In addressing this issue, the present study applies machine learning algorithms (MLAs), specifically multilayer perceptron (MLP) neural networks and the adaptive neuro-fuzzy inference system (ANFIS), to predict potential defects in railway vehicles and improve maintenance and repair strategies within the Iranian railway network. The findings reveal that ANFIS achieves superior predictive accuracy. Building on this, a mathematical model in combination with the Particle Swarm Optimization (PSO) algorithm was developed to optimise train allocation across stations and generate schedules aimed at reducing delays. The employed algorithms proved to be highly effective for predictive maintenance and repair of railway vehicles, ultimately contributing to delay reduction within the railway system.
Open Access: Yes
Integration of MULTIMOORA algorithm combined with circular q-rung orthopair fuzzy information for optimizing player positioning
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
The following paper presents a new analytical framework for the optimization of player positioning, a methodology with significant practical implications. The method implements the multi-objective optimization by ratio analysis with full multiplicative form (MULTIMOORA) in a decision-making context in which several non-commensurable performance variables have to be combined. The application of Dombi operationalizes the framework by prioritizing weighted aggregation operators coupled with circular q-rung orthopair fuzzy sets (Cq-ROFSs). The Cq-ROFSs allow multidimensional representation of uncertainty, and allow dynamic actions upon the fuzzy parameter q, such that both intuitionistic fuzzy sets and Pythagorean fuzzy sets are subsets. Two Dombi prioritized operators on Cq-ROFSs are thereby devised a Cq-ROFSs Dombi prioritized weighted averaging operator (Cq-ROFSDPWA) and a Cq-ROFSs Dombi prioritized weighted geometric operator (Cq-ROFSDPWG). Results from empirical experiments are reported that demonstrate the performance of the resulting methodology, highlighting its practical relevance. The fundamental properties of these operators are also examined. The proposed aggregation operators are applied within the MULTIMOORA technique to assess their effectiveness. Numerical examples demonstrate that the methods yield logical and consistent results across different decision-making scenarios. Comparative analyses further highlight the advantages of the Cq-ROFSDPWA and Cq-ROFSDPWG operators over existing approaches.
Open Access: Yes
Heart disease prediction with a feature-sensitized interpretable framework for the Internet of Medical Things sensors
Publication Name: Frontiers in Digital Health
Publication Date: 2025-01-01
Volume: 7
Issue: Unknown
Page Range: Unknown
Description:
Introduction: Cardiovascular health is increasingly at risk due to modern lifestyle factors such as obesity, smoking, stress, hypertension, and sedentary behavior. Post-pandemic health practices and medication side effects have further contributed to rising cases of early heart failure, particularly among individuals aged 25–40 years. This highlights the need for an automated and interpretable framework to predict heart disease at an early stage. Methods: In this study, body vitals acquired from a secondary dataset. Machine learning models including Support Vector Machine, Random Forest, Decision Tree, and Logistic Regression were employed for classification. Model performance was evaluated using accuracy, F1-score, and k-fold cross-validation. Results: Among the tested models, the Random Forest classifier demonstrated superior performance with an accuracy and F1-score of 0.955. The interpretability is enhanced with model predictions were explained using Local Interpretable Model-Agnostic Explanations (LIME) for local surrogates and SHAP values for global surrogates. SHAP decision plots provided clear insights into classification behaviour and feature contributions. Discussion/Conclusion: The proposed interpretable machine learning framework successfully predicts heart disease with high accuracy while maintaining transparency in decision-making. With the integration of sensor data with cloud-based analysis and explainable AI techniques, this study contributes to reducing the incidence of early heart failures and supports more reliable decision-making in healthcare applications.
Open Access: Yes
Data-driven decision-making framework for the evaluation of the traders in the stock market using cosine trigonometric single-valued neutrosophic approach
Publication Name: Journal of Mathematics and Computer Science
Publication Date: 2026-01-01
Volume: 41
Issue: 2
Page Range: 222-243
Description:
The cosine trigonometric single valued neutrosophic number (CT-SVNN) is a suitable expansion of the standard neutrosophic number. Single-valued neutrosophic sets (SVNSs) may effectively overcome three components: degree of truth, indeterminacy, and falsity. In recent years, the aggregation operator (AO) and its applications have undergone development. This study introduces a few new AOs for multi-attribute decision-making (MADM). We introduce a novel approach for cosine trigonometric SVNS (CT-SVNS) and CT-SVNS with normal (CT-SVNNS), which are SVNS extensions. It is also required to discuss the CT-SVNNS method fundamental features in this communication, such as idempotency, boundedness, commutativity and monotonicity. There are numerous CT-SVNNS operators that have been proposed, including CT-SVN normal weighted averaging (CT-SVNNWA), CT-SVN normal weighted geometric (CT-SVNNWG), generalized CT-SVNNWA (GCT-SVNNWA) and generalized CT-SVNNWG. A powerful strategy for solving the MADM problem is provided that makes use of new developed generalized operators. Through a case study, the value of the suggested MADM approach is demonstrated. The new strategy is shown using a market share problem, and the outcomes are contrasted and examined against an existing method. This combination of generalized AO was rated successful based on expert preferences. As a result, a varied collection of experts may be accepted.
Open Access: Yes
Coherent manipulation of spatial bright solitons of reflection and transmission using control fields of Milnor Gaussian polynomials
Publication Name: Frontiers in Physics
Publication Date: 2025-01-01
Volume: 13
Issue: Unknown
Page Range: Unknown
Description:
The generation of spatial bright solitons of reflection and transmission pulses and their intensities are investigated in a sodium atomic medium using Gaussian Milnor polynomial control fields. Significant bright and dark ring-shaped solitons are controlled by balancing nonlinearity and dispersion along two spatial coordinates. The intensity is more localized along one of the spatial coordinates due to larger nonlinearity and spread along other spatial coordinates due to smaller nonlinearity in the reflection pulse. A circular, crater-type bright soliton intensity is also maintained around the origin of the x and y coordinates, exhibiting varying intensity along the circumference. A large, bright intensity peak is observed around the origin, with the intensity minima at the center in reflection. The intensity peaks are enhanced in one of the spatial coordinates and localized in another coordinate in reflection. A large Gaussian-type bright solitonic intensity distribution is investigated at approximately (Formula presented.) throughout the variation along the x-axis in the transmission pulse pattern. The reflection and transmission pulse intensities vary from (Formula presented.) to (Formula presented.), and at least (Formula presented.) of the intensity of the incident pulse is lost by attenuation. The modified results are useful in optical communications, fiber optics, optical computation, signal processing, radar technology, and artificial neural networks.
Open Access: Yes
Enhancing confidence level in decision-making frameworks using fermatean fuzzy rough sets: Application in industry 4.0
Publication Name: Applied Soft Computing
Publication Date: 2026-01-01
Volume: 186
Issue: Unknown
Page Range: Unknown
Description:
Multidimensional decision-making has substituted traditional decision-making due to the increased risk and complexity involved in the decision processes and cognitive behaviors. Moreover, uncertainty management is necessary in the decision-making processes that involve the degree of confidence of the experts. Conflict assessment and resolution are paramount to the smooth functioning of the industrial ecosystem in such an automated dynamic environment. This research aims to create a multi-attribute decision-making (MADM) model in a hybrid fuzzy frame to evaluate and resolve conflicts in Industry 4.0. The MADM model dwells on three primary points, i.e., (i) how to efficiently manage ambiguity and interrelationships in MADM issues; (ii) how to encompass the mindset of the decision maker in all areas concerned; and (iii) how to demonstrate results in terms of acceptance and rejection rather than ranking issues when more than one factor is involved. The test data of a fermatean fuzzy set (FFS) with rough relations, which addresses upper and lower approximations, demonstrates the possible uncertainty of the information. A fermatean fuzzy rough set (FFRS) is initially defined within the model. Subsequently, an FFRS incorporating the operator's confidence level is delineated. This demonstrates the importance of FFRS in MADM contexts and suggests that they require further examination of their data processing regulations. Furthermore, we evaluate the accuracy and validity of the results by employing mean absolute errors, cosine similarity of the operators, and Spearman rank correlation. To illustrate the accuracy and validity of our method in the MADM context, we performed a comparative analysis. Finally, a practical illustration of the selection of Industry 4.0 technologies within the healthcare sector exemplifies the efficacy and potential of this innovative approach for future applications of MADM. The intricate multi-stakeholder conflicts and data uncertainties presented by Industry 4.0 environments, especially regarding healthcare technology implementation, will be examined using the research framework illustrated in Fig. 1.
Open Access: Yes
A complete ranking principle for trapezoidal Hesitant fuzzy sets and its application in solving trapezoidal Hesitant fuzzy linear programming problems
Publication Name: Complex and Intelligent Systems
Publication Date: 2025-12-01
Volume: 11
Issue: 12
Page Range: Unknown
Description:
Decision-making is significant in economics, education, management, industries, and many real-life situations. Every decision-making problem does not have crisp parameters and restrictions, and may have uncertainties and qualitative information. Trapezoidal Hesitant fuzzy sets are highly beneficial for dealing with situations that are more uncertain and have some qualitative aspects. First, this paper introduces four score functions on the class of trapezoidal hesitant fuzzy elements, namely mean-position, right-spread, inference, and support position score function to define a complete ranking principle on trapezoidal hesitant fuzzy elements. Secondly, we propose a complete ranking principle on the class of trapezoidal Hesitant fuzzy sets by generalizing the four proposed score functions on the set of trapezoidal hesitant fuzzy elements. Thirdly, we extend our work to suggest another complete ranking principle on the new important sub-class of the trapezoidal Hesitant fuzzy set. Fourthly, we compare our proposed complete ranking principle with a few existing important ranking principles discussed in the trapezoidal Hesitant fuzzy sets (TrHFS) class. Further, we establish two new simplex algorithms on a special class of trapezoidal Hesitant fuzzy sets to solve fully Hesitant fuzzy linear programming problems (HFLPP). These new algorithms incorporate the proposed complete ranking principle on the trapezoidal Hesitant fuzzy set. Finally, we solve a few numerical examples of linear programming problems with fully Hesitant fuzzy information to show the applicability and potentiality of the proposed linear programming method. Complete ranking principle in the class of TrHFS and the use of complete ranking principle in solving a fully HFLPP is proposed for the first time in the literature.
Open Access: Yes
OWA operators and probabilities under hypersoft set environments
Publication Name: Systems and Soft Computing
Publication Date: 2025-12-01
Volume: 7
Issue: Unknown
Page Range: Unknown
Description:
This study proposes novel extensions to overcome the limitations of classical aggregation methods, namely ordered weighted averaging (OWA) and probabilistic OWA (POWA) operators, in handling hierarchical or subdivided attributes under uncertainty within the hypersoft set (HS) framework, resulting in the hypersoft set-based OWA (HS-OWA) and hypersoft set-based POWA (HS-POWA) operators. These extensions (HS-OWA and HS-POWA operators) preserve sub-attribute information, enhance decision accuracy, and handle uncertainty, including fuzzy, intuitionistic, and neutrosophic data. We formalize the mathematical definitions and theoretical properties of HS-OWA and HS-POWA, demonstrating their practical applicability through a case study of sustainable wastewater treatment method selection. Additionally, we generalize the proposed operators under various fuzzy extensions, including intuitionistic fuzzy sets (IFS), pythagorean fuzzy sets (PFS), q-Rung orthopair fuzzy sets (q-ROFS), and neutrosophic sets (NS), allowing flexible modeling of uncertainty, hesitation, and conflict in expert assessments. The results from our study validate the superiority of the proposed framework in aggregating distributed evaluations while preserving semantic depth and interoperability. The proposed operators are effective in complex multi-criteria and group decision-making problems, such as sustainable technology assessment and policy-making, and provide a robust framework for future research in dynamic and large-scale MCDM applications.
Open Access: Yes
Digital Transformation in Taiwan’s Insurance Industries for MABAC Technology Based on Circular Modified Fuzzy Choquet Frank Network Data Envelopment Analysis
Publication Name: International Journal of Analysis and Applications
Publication Date: 2025-01-01
Volume: 23
Issue: Unknown
Page Range: Unknown
Description:
Fuzzy set theory has significant and dominant applications in Taiwan’s insurance industry, especially in fields involving decision-making, uncertainty, and risk assessment. Providing the complexity and problems in assessing factors, for instance, natural disaster risks, customer creditworthiness, or health conditions, traditional binary logic often falls short. Taiwanese insurers have adopted fuzzy logic systems to enhance fraud detection, premium pricing, and privilege evaluations by catching the indistinctness characteristic in human ruling and imperfect data. The Taiwan insurance industry is a dynamic and spirited module of the commercial sector, contributing meaningfully to risk management and economic stability. For this, we study to propose an assessment of the proficiency of insurance enterprises using Network Data Envelopment Analysis. Toward this end, the frank operational laws for circular Pythagorean fuzzy (CPF) uncertainty are applied. Moreover, the CPF Choquet Frank averaging (CPFCFA) operator and CPF Choquet Frank geometric (CPFCFG) operator with three dominant properties for each operator have been studied. The study deliberates the multi-attributive border approximation area comparison (MABAC) model and verifies it with the help of numerical examples. This study enhances the industry’s efficiency to offer adapted insurance products and handle risks precisely, aligning with Taiwan’s push toward intelligent financial services and digital transformation. In the following, we establish the decision-making performance for assessing the proficiency of insurance enterprises using the network data envelopment analysis (NDEA) technique. Finally, we examine the ranking values of offered representations to compare them with the ranking values of prevailing models to show the capability and efficacy of the originated approaches.
Open Access: Yes
Innovating the Pilot Project Based on Multi-Attribute Group Decision-Making and Some Prioritized Aggregation Operators for Complex Pythagorean Fuzzy Information
Publication Name: Fuzzy Information and Engineering
Publication Date: 2025-01-01
Volume: 17
Issue: 3
Page Range: 261-283
Description:
Gathering information from a real-life scenario is a very difficult process due to involvement of the multiple criteria and human opinion. A complex Pythagorean fuzzy set (CPyFS) is an interesting tool to deal with uncertainty while gathering information from human opinion involved in real-life scenarios. But, the aggregation of the information gathered by CPyFS becomes very hectic. Several aggregation operators (AOs) aggregate the information in the form of complex Pythagorean fuzzy values (CPyFVs). However, they lack the prioritization of attributes according to their weights. In this article, an interesting new class of AOs including complex Pythagorean fuzzy (CPyF) prioritized averaging operator (CPyFPAO) and CPyF prioritized geometric operator (CPyFPGO) is introduced. Basic and necessary properties of the introduced AOs are observed. Furthermore, the case study is discussed where the introduced AOs are applied to seek the most suitable optimized site for starting a pilot health project with the help of the multi-attribute group decision-making (MAGDM) process. The results obtained from all proposed AOs are analyzed and compared with some existing AOs. All the analyses are explained with the help of the tabulated data and graphs.
Open Access: Yes
MABAC model based on linguistic (p, q)-rung orthopair fuzzy Z-number and their application in green supply chain management
Publication Name: International Journal of Cognitive Computing in Engineering
Publication Date: 2026-12-01
Volume: 7
Issue: Unknown
Page Range: 247-267
Description:
The problem and complication arise from the growing environmental inefficiencies and concerns in traditional supply chains, for instance, poor accountability, excessive waste, and lack of transparency. The green supply chain practices aim to reduce or minimize the environmental impact of supply chain activities, but these efforts often face problems, for example, difficulty in monitoring sustainability performance, data manipulation, and limited traceability across numerous stakeholders. The main problem is that without effective techniques to verify and track eco-friendly practices, enterprises struggle to utilize and enforce green initiatives reliably. The blockchain technique is being derived as a solution because of its capability to give decentralized, transparent, and immutable records of processes and transactions. By integrating the blockchain into green supply chain practices, we aim to design the model of linguistic (p, q)-rung orthopair fuzzy Z-number sets with algebraic and Sugeno-Weber operational laws for the construction of the power weighted averaging operator and power weighted geometric operator. These operators can be used in the utilization of the multi-attributive border approximation area comparison model, which is also explained step-by-step with the help of examples to simplify the supremacy and validity of the invented model by comparing their ranking values with the ranking values of the existing approaches.
Open Access: Yes
Data-driven Floyd’s algorithm with AirQo monitoring device for optimizing transportation routes in an uncertain environment
Publication Name: Engineering Applications of Artificial Intelligence
Publication Date: 2026-01-01
Volume: 163
Issue: Unknown
Page Range: Unknown
Description:
This manuscript presents a novel All-pair shortest path algorithm that enhances Floyd’s method by integrating a soft computing-based decision model tailored for transportation routing in an uncertain environment. The routing problem is formulated as a graph, where the edges are aggregated into a single representative weight from multiple influencing factors using an aggregation operator and the score function. These weights represent pollution levels based on air quality data collected by the AirQo monitoring device along different route segments. By integrating decision making method, the enhanced Floyd’s algorithm is then used to compute the most effective route between a defined source and destination. The proposed method supports healthier travel choices by identifying routes with comparatively cleaner air. Preliminary simulations indicate that the suggested technique facilitates more informed route selection compared to conventional approaches. The uniqueness of this method lies in its integration of classical graph theory with decision-making for real-time environmental sensing, offering reduced exposure to pollutants and supporting cleaner, safer mobility in urban environments.
Open Access: Yes
Entity-relation enhanced bidirectional information fusion for relational triples extraction
Publication Name: Engineering Applications of Artificial Intelligence
Publication Date: 2026-01-01
Volume: 163
Issue: Unknown
Page Range: Unknown
Description:
Relational triple extraction refers to identifying triples consisting of entities and relations form unstructured texts. The existing studies usually adopt an unidirectional extraction strategy, which fails to fully explore the semantic information related to entities and relations. And they rely heavily on initial extraction results when conducting multi-step extraction. To address this issue, we propose a novel Entity-Relation Enhanced Bidirectional Information Fusion approach (ER-EBIF). Specifically, we adopt a bidirectional extraction strategy of ”entity-to-relation” and ”relation-to-entity” to identify triples. One branch extracts potential relations, then extracts entities associated with those relations. The other branch initially extracts the potential subjects and objects as well as subsequently extracts relations between pairs of entities consisting of subjects and objects. Moreover, the contextual information is enhanced with a self-attention mechanism by integrating the information of potential relations and potential entities to better exploit the semantic information of entities and relations. Extensive experimental results on various datasets show that ER-EBIF exhibits better performance than other baselines and effectiveness in addressing the issue of dependency on initial results in multi-step extraction.
Open Access: Yes
A hybrid five-way decision architecture integrating q-ROFS with outranking relations and Bayesian risk optimization
Publication Name: Results in Engineering
Publication Date: 2025-12-01
Volume: 28
Issue: Unknown
Page Range: Unknown
Description:
To address the complex challenges of green sustainable agriculture arising from regional economic growth, this study proposes a novel five-way decision (F-WD) architecture. The framework is evaluated using a Bayesian risk mechanism to minimize expected losses, leveraging the flexibility of q-rung orthopair fuzzy logic ( q ¨ ≥ 1 ) combined with outranking relations and decision-theoretic rough sets (DTRS). This approach defines alternatives across five semantically interpretable regions, extending the conventional three-way decision (T-WD) framework to provide a more granular and versatile classification system. Outranking relations enable partial preference modeling among alternatives by incorporating dominance and incomparability, offering nuanced judgments when criteria conflict or are incommensurable unlike traditional ranking methods. The study begins by formulating F-WD specific relative gain and loss functions, laying the groundwork for the five zone decision-making process. It further validates the reliability of threshold values, their properties, and their role in defining decision boundaries. Compared to conventional methods like TOPSIS, Fuzzy AHP, T-WD-DTRS, IVIFS-ELECTRE, and the proposed technique demonstrates superior decision granularity, interpretability, and classification accuracy. These results underscore the method's credibility, soundness, and practical effectiveness in handling uncertainty, hesitation, and compromise in risk-sensitive environments. The q-ROFS-based F-WD model not only outperforms existing approaches but also serves as a powerful, adaptive tool for sustainable development, strategic planning, and policymaking in complex scenarios.
Open Access: Yes
Scenario-driven decision models for rare element waste management by integrating koch snowflake fuzzy sets and euclidean expert weighting
Publication Name: Sustainable Futures
Publication Date: 2025-12-01
Volume: 10
Issue: Unknown
Page Range: Unknown
Description:
The most critical factors must be determined to effectively manage environmental wastes generated during the extraction of rare elements. Otherwise, businesses may not be able to effectively manage their limited financial and human resources. This situation negatively affects the financial performance of the projects. The limited number of existing studies in the literature causes environmental risks to be insufficiently managed and recycling processes to be unoptimized. This study aims to determine priority strategies to increase the effectiveness of rare element waste management processes. Comprehensive and original decision-making models are created under three different scenarios. Koch Snowflake fuzzy sets, Euclidean based expert weighting and cognitive information modelling and analysis system (CIMAS) approaches are integrated in this model. The main contribution of this study is that a new type of fuzzy numbers called Koch Snowflake fuzzy sets is developed by considering the concept of fractal numbers. Fractal geometry is a powerful tool for modelling complex and dynamic systems. Hence, these new sets provide more flexible and more detailed uncertainty modelling. Moreover, considering different scenarios dynamic strategies can be developed that can adapt to changing conditions, such as pandemics or trade wars. The findings denote that technological developments are determined as the most critical factor under normal conditions. In the scenario where trade wars occur, it is revealed that political and regulatory measures should be addressed as a priority. In the event of a new epidemic disease such as COVID-19, it is concluded that more importance should be given to long-term storage strategies.
Open Access: Yes
Reliable power management and predictive analysis of domestic appliances with insights of XAI
Publication Name: Energy Reports
Publication Date: 2025-12-01
Volume: 14
Issue: Unknown
Page Range: 3704-3718
Description:
The unanimous focus of the sustainable technological development is energy conservation and environmental friendly production. Power management is an essential aspect of sustainable development. It not only support energy production and conservation, but also increases the life time of domestic appliances and thereby reducing the global electronic wastage. The existing systems involving Artificial Intelligence (AI) were mere prediction models, without the evidence on the detailing behind the prediction. Traditional AI systems have focused on predictive analysis but often lack transparency in decision-making and limiting consumer trust. This study proposes a solution combining remote power monitoring with the ZigBee module and Explainable Artificial Intelligence (XAI) to offer both predictive accuracy and interpretability. XAI models are more consumer oriented in every area of application, similar to the problem discussed, which tells about the impact of various parameters in power management in domestic appliances. Local Interpretable Model Agonistic Explainer(LIME) and SHAP explainer are used in the proposed work, providing explainability in the local and global surrogates. The proposed work applies various regression models such as Decision Tree (DT), Random Forest(RF), Support Vector Regressor (SVR), Gradient Boost Regressor (GBR) and Extreme Graident Boost Regressor (XGBR). The RF provides the best R2-Score of 94.71% , which is 1.5%–3.0% more than the rest of the models, and also with variance score of 68.82% , had been chosen for explainability. This study demonstrates how XAI can improve transparency and reliability in AI-powered domestic energy systems, offering actionable insights for more sustainable power consumption.
Open Access: Yes
A data-driven approach to tackling academic stress-coping and mental health issues in college students using spherical fuzzy MARCOS methodology
Publication Name: Applied Soft Computing
Publication Date: 2025-12-01
Volume: 185
Issue: Unknown
Page Range: Unknown
Description:
The drastically developing nature of the knowledge economy and the rising need for top-notch expertise have placed tremendous pressure on college students. As higher education becomes more accessible, masses of students are enrolling in colleges, which puts additional pressure on colleges and institutions; as a result, they cannot provide adequate resources to the students. As the class size increases, many students require mental health assistance, academic guidance, and financial aid, which then puts pressure on the teachers and the facilities. This flood of students overloads the facilities, resulting in it becoming more challenging to provide attention and concern, leading many students to feel overlooked and affecting their mental health. Due to not getting timely support, students may find it challenging to handle their academic responsibilities. Moreover, the students face a heavy workload, unclear guidance, and limited resource access. The objective of this study is to develop a structured, data-driven decision-making framework for systematically evaluating and improving student mental health and academic stress-coping strategies in a college setting. To address this, a comprehensive decision-making structure, measurement of alternatives, and ranking according to the compromised solution (MARCOS) within the spherical fuzzy (SF) environment, has been applied, which evaluates the key factors causing mental health issues by comparing the ideal and anti-ideal alternatives. The novelty of the proposed approach lies in leveraging the SF framework's explicit ability to model hesitation (abstinence) alongside truth and falsity degrees, enabling more accurate representation of subjective psychological assessments compared to traditional fuzzy models. Furthermore, the method calculates utility functions corresponding to each alternative (coping technique), prioritizes the strategies, and selects the most effective intervention. The results reveal that personalized mental health plans emerged as the top-ranked coping strategy, highlighting the importance of tailored support in culturally and contextually diverse academic environments.
Open Access: Yes
The impact of the application of artificial intelligence preparedness on sustainable development goals: An empirical analysis
Publication Name: Multidisciplinary Science Journal
Publication Date: 2025-05-01
Volume: 8
Issue: 5
Page Range: Unknown
Description:
Artificial Intelligence (AI) is rapidly transforming economies and societies around the world. As AI is increasingly being invested in, some countries are developing specific strategies for AI development. These countries are striving to improve their competitiveness and achieve greater economic growth by becoming leaders in AI. In addition to AI development, countries are striving to achieve the Sustainable Development Goals (SDGs). In achieving these goals, countries are concerned about protecting the environment and preserving the resources they have for future generations. This paper examines the impact of AI adoption on sustainable development, with a focus on progress toward the United Nations SDGs. Therefore, this paper analyzed how the willingness of countries to use AI affects the achievement of sustainable development in those countries. Accordingly, the AI Preparedness Index and Sustainable Development Goals indicators were used for 158 countries in the world. The relationship between these variables was examined using multiple regression analysis. The results of the multiple regression analysis show that the willingness of countries to apply AI affects the realization of the SDGs of those countries. However, not all dimensions of readiness for the application of AI have an impact on the goals of sustainable development. It has been shown that the dimensions of digital infrastructure and human capital and labor market policies have the greatest influence on the SDGs. Based on this; countries must strengthen these two dimensions regarding the application of AI in order to realize the SDGs. By strengthening the potential for AI development, these countries are improving sustainability through achieving the SDGs.
Open Access: Yes
Assessment of ecotourism potential in rural settlements in the function of rural development
Publication Name: Cogent Social Sciences
Publication Date: 2025-01-01
Volume: 11
Issue: 1
Page Range: Unknown
Description:
To preserve resources for future generations and promote rural development, supporting ecotourism is essential. This paper provides guidelines for developing ecotourism, highlighting its role in environmental conservation. While mass tourism benefits rural communities, it can cause significant environmental harm. Therefore, this research promotes ecotourism as a sustainable alternative. In rural areas, ecotourism supports development by responsibly using natural resources. The study focuses on the potential of rural settlements in the Semberija region of Bosnia and Herzegovina, assessing their capacity for ecotourism to aid local development. A decision model was developed, considering four main criteria - natural, infrastructure, socio-cultural, and economic - and their sub-criteria. This model evaluates six rural communities’ ecotourism potential. To determine the importance of each criterion, a fuzzy weighting method with the Bonferroni mean operator was used, revealing economic factors as the most influential. The fuzzy ranking method then ranked the settlements, with Amajlije identified as having the highest ecotourism potential. The findings suggest that promoting ecotourism in Amajlije and similar communities can support sustainable rural development, balancing environmental preservation with economic growth.
Open Access: Yes
Mathematical Simulation for Influence of Thermocapillary Radiative MHD Unsteady Couple Stress Ternary Hybrid Nanofluid on Stretching Parallel Surface
Publication Name: Contemporary Mathematics Singapore
Publication Date: 2025-01-01
Volume: 6
Issue: 6
Page Range: 7636-7653
Description:
This study aims to provide a thorough mathematical simulation of the effects of heat radiation and thermocapillarity on the time-dependent flow of couple stress ternary hybrid nanofluid across a stretching parallel surface in magneto-hydrodynamics. The ternary hybrid nanofluid consists of Ag, TiO2 , Al2 O3 nanoparticles dispersed within a base fluid, blood, enhancing its thermal performance. The governing partial differential equations are converted into a system of nonlinear ordinary differential equations by applying the proper similarity transformations to model the flow’s unstable behavior. After that, the Homotopy Analysis Method is used to solve these equations semi-analytically. The intricate interactions between radiative heat transport, thermocapillary forces induced by surface tension gradients, Lorentz force from the applied magnetic field, and couple stress effects are all captured in the simulation. The influence of main dimensionless parameters, including the magnetic parameter, couple stress parameter, nanoparticle volume fractions, dimensionless film thickness, unsteady parameter, thermal radiation parameter and Eckert number, on velocity profile, temperature profile, skin friction and Nusselt number in the form of graphs. According to the results, radiation improves the properties of heat transmission, whereas thermocapillarity dramatically changes the flow and thermal boundary layers. Furthermore, the fluid velocity is suppressed by the occurrence of magnetic fields and couple stress, providing information about possible control mechanisms in thermal management systems. The results’ graphical and tabular representations demonstrate how sensitive the temperature and velocity fields are to the physical parameters at play. These findings offer significant new insights into thermal management technologies and energy systems that employ complex nanofluid compositions.
Open Access: Yes
Ethical Dimensions in Supplier Selection Sustainability: Introducing the Modified MARCOS Method via Fuzzy-Rough Set with the LMAW Approach
Publication Name: Contemporary Mathematics Singapore
Publication Date: 2025-01-01
Volume: 6
Issue: 6
Page Range: 7899-7924
Description:
This research presents a novel approach to supplier selection by integrating economic, environmental, and ethical criteria. The case study of Company 3B, a food production company, illustrates this process. Expert decisionmaking, using a fuzzy-rough approach, is supported by the fuzzy-rough Logarithm Methodology of Additive Weights (LMAW) and the fuzzy-rough modified Measurement Alternatives and Ranking according to Compromise Solution (MARCOS) methods. The fuzzy-rough LMAW method helps determine the importance of criteria, revealing that experts consider the economic criterion the most significant. The Modified MARCOS (M-MARCOS), a simplified version of the MARCOS method, is used to rank suppliers. Results show that Supplier S3 performs the best. These findings are validated through comparisons with other fuzzy-rough methods and a sensitivity analysis. With the MARCOS method comparisons confirming a consistent ranking order, this paper advances supplier selection methodology by introducing a novel approach and improving the usability of the MARCOS method through modifications.
Open Access: Yes
Fluctuating Free Convection Flow of Casson Dusty Fluid in an Inclined Microchannel Under Wall Shear Stress and an Inclined Magnetic Field
Publication Name: Contemporary Mathematics Singapore
Publication Date: 2025-01-01
Volume: 6
Issue: 6
Page Range: 7601-7618
Description:
This study examines the unsteady free convection flow of Casson dusty fluid within an inclined microchannel under the influence of wall shear stress and an inclined magnetic field. The fluid is assumed to contain uniformly dispersed electrically conductive dust particles, and heat is applied via Newtonian heating at one boundary. The governing partial differential equations representing the motion of both fluid and dust phases are derived and solved using the Poincaré-Lighthill Perturbation Technique (PLPT). Key physical parameters such as the Casson fluid parameter, Grashof number, magnetic field inclination, radiation, and dusty fluid interaction parameter are varied to analyze their effect on velocity and temperature profiles. Results reveal that increasing the Casson parameter reduces fluid velocity, while higher Grashof numbers and radiation levels enhance it. The magnetic field generates Lorentz forces that oppose the motion, thereby reducing both fluid and dust particle velocities. The inclined magnetic field and Newtonian heating significantly influence thermal and flow behavior. These findings have practical implications in microfluidics, industrial coatings, biomedical flows, and heat management systems, where controlling dusty fluid dynamics under external fields is crucial.
Open Access: Yes
Numerical analysis of MHD ternary nanofluid flow with heat transfer in porous convergent/divergent channel
Publication Name: Journal of Thermal Analysis and Calorimetry
Publication Date: 2025-11-01
Volume: 150
Issue: 23
Page Range: 19481-19489
Description:
In this research, we investigate the flow of ternary nanofluids under the influence of magnetic field in converging/diverging channel. The fluid assumed to be viscous, incompressible, and electrically conducting. The effect of Lorentz force on fluid motion is systematically examined. For practical applications, stretching and shrinking channel are considered. Using similarity transformation, the governing partial differential equations (PDEs) are reduced to ordinary differential equations (ODEs), which are then solved numerically with the ND-Solve technique. This method effectively handles the highly nonlinear equations and provides accurate results. The velocity and temperature profiles are presented graphically for various physical parameters, while skin friction and Nusselt number are analyzed. The results reveal that an increase in the magnetic parameter reduces the fluid velocity and enhances the temperature in both convergent/divergent channels. Furthermore, ternary nanofluids exhibit a stronger impact compared to nano and hybrid nanofluids.
Open Access: Yes
APPLICATION OF FUZZY MCDM IN SELECTING ECO-FRIENDLY MATERIALS FOR ELECTRIC VEHICLE INTERIORS
Publication Name: Journal of Applied Engineering Science
Publication Date: 2025-09-15
Volume: 23
Issue: 3
Page Range: 504-522
Description:
The growing demand for sustainable solutions in the automotive industry has led to a significant focus on eco-friendly materials for electric vehicle (EV) interiors. This research paper explores the application of Fuzzy Multi-Criteria Decision-Making (MCDM) in selecting optimal eco-friendly materials for EV interiors. Fuzzy MCDM provides a robust framework to handle the inherent uncertainty and subjectivity in evaluating multiple criteria such as recyclability, durability, strength, comfort, aesthetic appeal, carbon footprint, price, energy requirements, and complexity in manufacturing. By employing a combination of Fuzzy-Entropy and Fuzzy-TOPSIS, this study aims to prioritize materials that offer the best balance of environmental sustainability and performance. Entropy is employed to evaluate the criteria weights, whereas TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is applied to select the ideal sustainable materials for EV interiors and to rate the alternatives. The final result reveals that Polyethylene Terephthalate is the most suitable material alternative for EV interiors, significantly enhancing the sustainability of the automotive industry. In contrast, Bamboo Fiber Composite ranks the lowest among the alternatives, indicating it is the least favorable option in the group. The final outcomes from the fuzzy-entropy-TOPSIS model are also compared to six others solo MCDM models and the ranking stability is also verified through sensitivity analysis.
Open Access: Yes
DOI: 10.5937/jaes0-57423
A Data-Driven MCDM Approach to Evaluating Sexual Harassment Factors in Educational Settings: Integrating OrdPA-F with Triangular Intuitionistic Fuzzy MARCOS Methods
Publication Name: Boletim Da Sociedade Paranaense De Matematica
Publication Date: 2025-01-16
Volume: 43
Issue: Unknown
Page Range: Unknown
Description:
This study examines the impact of ordering elements on sexual harassment in educational environments and assesses legislative measures aimed at improving the safety of female students in schools and universities. The combination of the ordinal priority approach with fuzzy information and intuitionistic fuzzy MARCOS within a decision framework facilitates the identification of numerous critical components. Initially, we employ an ordinal priority method on fuzzy data to determine the weights of the attributes. Subsequently, we employ the intuitionistic fuzzy MARCOS methodology to assess the prioritised aspects. The formulation of triangular fuzzy numbers for analytical objectives enhances decision-making. This study employs Dombi aggregation operations to comprehensively assess the influence of these factors on school sexual harassment. This research presents a systematic, data-informed approach to assist legislators in developing successful strategies for mitigating sexual harassment and improving safety in educational settings.
Open Access: Yes
DOI: 10.5269/bspm.78139
Integrated Rough AHP and Neural Network Model for Mobile Phone Selection with Big Data Under Uncertainty
Publication Name: Journal of Intelligent and Fuzzy Systems
Publication Date: 2025-12-01
Volume: 49
Issue: 6
Page Range: 1414-1427
Description:
This paper applied an integrated approach to Multi-Attribute Decision Making (MADM) by combining the Rough Analytic Hierarchy Process (RAHP) and Neural Network, specifically a Multi-Layer Perceptron (MLP) for a specific problem of smartphone selection. The Rough Analytic Hierarchy Process, grounded in rough set theory, proves adept at handling uncertainties in decision-making processes. Through the integration of RAHP and MLP, this study provides a comprehensive framework for ranking mobile phone criteria, focusing on camera quality, selfie capabilities, audio performance, display features, battery life, and pricing. The practical example employed demonstrates the applicability of the proposed methodology in real-world decision-making scenarios, the fusion of RAHP and MLP emerges as a potent solution for Multiple Attribute Decision Making (MADM) problems, offering decision-makers confidence in navigating intricate scenarios. This integrated approach signifies a new era of robust decision-making, enhancing outcomes across diverse domains by synergizing structured prioritization and uncertainty management. The paper proceeds with a literature review, outlining existing approaches in decision-making scenarios. The methods section details the operations with rough numbers, the Rough Analytic Hierarchy Process, and the Multi-Layer Perceptron. A numerical example of mobile phone selection is presented, illustrating the application of the integrated approach. In the presented numerical example, two scenarios are provided: one without a price criterion and another with a price criterion. In the price-less scenario, the Honor Magic5 Pro is chosen, while in the scenario considering price, the Oppo Find X6 Pro is selected as the best option.
Open Access: Yes
Decision Support System for Financial and Accounting Performance Assessment in Manufacturing Industries
Publication Name: International Journal of Computational Intelligence Systems
Publication Date: 2025-12-01
Volume: 18
Issue: 1
Page Range: Unknown
Description:
The increasing intricacy of financial and accounting decisions within manufacturing sectors necessitates comprehensive, data-driven assessment tools. This study examines the assessment of financial and accounting performance in manufacturing firms amidst uncertainty, emphasizing the importance of reliable and transparent decision support. A novel decision support system is proposed, integrating advanced multi-criteria decision-making techniques. Picture fuzzy sets are utilized to represent uncertainty and hesitation in expert evaluations by depicting positive, neutral, and negative assessments with different levels of indeterminacy. A dual weighting approach is utilized, employing the logarithmic percentage change-driven objective weighting method to quantify the dispersion and relevance of criterion data, while the ranking comparison method systematically integrates expert preferences. The MARCOS method is employed to assess alternatives and rank firms according to compromise solutions. A case study of manufacturing firms demonstrates the model’s applicability, revealing that profitability, liquidity, and efficiency of costs are the primary financial and accounting measures. The automotive part supplier has been recognized as the best option due to its emphasis on liquidity ratios and efficiency in operations, enabling it to fulfill supply commitments and mitigate risks related to profit margin limitations and quality compliance costs. The sensitivity and comparative analyses illustrate the system’s endurance and adaptability under different circumstances and stakeholder perspectives.
Open Access: Yes
Correction: A novel numerical investigation of fiber Bragg gratings with dispersive reflectivity having polynomial law of nonlinearity (Scientific Reports, (2025), 15, 1, (31110), 10.1038/s41598-025-12437-1)
Publication Name: Scientific Reports
Publication Date: 2025-12-01
Volume: 15
Issue: 1
Page Range: Unknown
Description:
Correction to: Scientific Reportshttps://doi.org/10.1038/s41598-025-12437-1, published online 24 August 2025 The original version of this Article contained an error in the name of author Mansour Shrahili, which was incorrectly given as Mansour Shrahilii. The original Article has been corrected.
Open Access: Yes
Advancing decision-making frameworks: Generalized distance measures in complex fuzzy set environments for enhanced precision and robustness
Publication Name: Systems and Soft Computing
Publication Date: 2025-12-01
Volume: 7
Issue: Unknown
Page Range: Unknown
Description:
A complex fuzzy distance measure (CFDM) is a way to quantify the dissimilarity or similarity between two complex fuzzy sets (CFSs). This measure often considers both the membership values and the degree of overlap between sets to compute the distance. However, CFDMs are not capable of capturing the hesitancy or uncertainty inherent in real-life problems. To overcome this difficulty, we present generalized notions of some existing distance measures (DMs), such as Zhang DM and Zeeshan DM within the framework of CFSs. The newly defined DMs are said to be complex fuzzy generalized Zhang Hesitance DM (CFGZHDM), complex fuzzy generalized weighted Zhang Hesitance DM (CFGWZHDM), complex fuzzy generalized Zeeshan Hesitance DM (CFGZHDM), and complex fuzzy generalized weighted Zeeshan Hesitance DM (CFGWZHDM). Several new set-theoretic operations and fundamental mathematical results are formally defined and developed. These are built upon the framework of the proposed decision-making models to strengthen their applicability and theoretical foundation. We utilized the proposed generalized CFDMs in applications to decision-making problems. We proposed a new decision-making algorithm that offers a flexible and nuanced approach to selecting exemplary students by considering the fuzzy and overlapping nature of attributes and allowing for uncertainty in the selection process. Furthermore, a comparative analysis is conducted between the proposed models, evaluating their performance and effectiveness about several existing fuzzy models. This comparison aims to highlight the strengths, differences, and potential advantages of the newly proposed models over conventional methods. Moreover, the newly defined decision-making approaches illustrate clear improvements over existing techniques. While traditional techniques fail to provide meaningful ranking values, our proposed approaches produce non-zero scores such as 0.13, 0.30, and 0.10, leading to a valid ordering of alternatives. When weighted information is considered, the effectiveness is further enhanced, yielding higher score values (0.80, 0.85, and 0.81) and more stable rankings.
Open Access: Yes
Enhancing urban solar photovoltaic system performance evaluation through a disc spherical fuzzy aggregation framework
Publication Name: Journal of Computational Science
Publication Date: 2026-01-01
Volume: 93
Issue: Unknown
Page Range: Unknown
Description:
The integration of solar photovoltaic (PV) systems in urban environments promises great potential for sustainable energy applications. However, the unique characteristics of cities, the varieties of weather that occur at the place, and technology inefficiency make performance evaluation difficult. This paper sought to address the pressing need for a robust performance evaluation framework for urban solar PV systems by developing a disc spherical fuzzy aggregation framework. It develops basic algebraic aggregation operations in the framework of the disc spherical fuzzy set (D-SFSs), proving their completeness and describing their essential characteristics. These new operators conceived to operate on D-SFSs furnish theoretical robustness and provide the foundation for decisions made. A shining novel disc spherical fuzzy method is developed namely combinative distance-based assessment (CODAS) in D-SFS. A case study regarding the application of this model in the assessment of performance by urban solar PV systems is being conducted, thus proving the application aspect. Results come out positive in interpreting the decision-making dilemma and differences among several experts. This would, therefore, encourage various sectors to expand the use of D-SFSs in decision support systems and similar areas by showing how useful they can be in actual situations.
Open Access: Yes
Cluster analysis selecting tools using quadri partitioned Pythagorean neutrosophic normal interval-valued set with an aggregation operators
Publication Name: Journal of Mathematics and Computer Science
Publication Date: 2025-01-01
Volume: 41
Issue: 4
Page Range: 487-518
Description:
The goal of a quadri partitioned Pythagorean neutrosophic normal interval-valued fuzzy set (QPPNNIVFS) is to provide the neutrosophic sets a more comprehensive mathematical foundation. QPPNNIVFS divides the indeterminacy component into unknown and contradiction classes. The several aggregating operations that have been understood thus far are discussed here. The fuzzy weighted QPPNNIVFW averaging (QPPNNIVFWA), QPPNNIVFW geometric (QPPNNIVFWG), generalized QPPNNIVFW averaging (GQPPNNIVFWA) and generalized QPPNNIVFW geometric (GQPPNNIVFWG) are considered as a novel concept. We show that algebraic structures like associative, distributive, idempotent, bounded, commutative, and monotonic characteristics are satisfied by QPPNNIVFSs. We illustrate the practical applications of increased Euclidean distance, Hamming distance, score, and accuracy values. Unless there is a mathematical justification for selecting one cluster technique over another, the clustering strategy must be selected empirically. An algorithm that performs well on one set of data will not perform well on another. There are several approaches of conducting cluster analysis. These include social network analysis, distribution-based, density-based, centroid-based and hierarchical. Therefore, it is clear that the natural number θ has a big impact on the models. To illustrate the comparison analysis, sensitivity analysis and the validity of our suggested methodologies are also conducted. The outcomes will be very helpful to decision makers in handling uncertain and conflicting data effectively.
Open Access: Yes
A vision explainability method for image captioning using transformer decoder attention maps
Publication Name: Methodsx
Publication Date: 2025-12-01
Volume: 15
Issue: Unknown
Page Range: Unknown
Description:
Image Captioning is a crucial task that enables systems to generate descriptive sentences for visual content. Though image captioning systems bloom at the intersection of Computer Vision and Natural Language Processing, these models act mostly as black boxes offering little or no insight into how captions are derived. We present a novel explainable image captioning framework that integrates a Convolutional Neural Network encoder with a Transformer decoder. Attention-based heatmaps are used to explain the visuals offering transparency in the decision making process. The method evaluates captioning quality and interpretability on the MS COCO dataset using BLEU, METEOR, CIDER and SPICE. The method enhances the trustworthiness and transparency, making it reliable for applications like healthcare, education, security, surveillance and forecasting.A reproducible method for integrating visual explainability into image captioning exploring transformer decoder attention maps.The method contributes to the growing body of eXplainable AI (XAI) by addressing the transparency gap in vision-language modelsBalance performance with interpretability paving the way for more transparent and trustworthy AI systems.
Open Access: Yes
Spectral-aware CNN with learnable biorthogonal units and depthwise convolutions for multi-class blood cell classification
Publication Name: Methodsx
Publication Date: 2025-12-01
Volume: 15
Issue: Unknown
Page Range: Unknown
Description:
For effective and early diagnosis of diseases such as leukemia and anemia, accurate classification and interpretation of peripheral blood cells are critical. A novel hybrid deep learning model is proposed in this study for multi-class blood cell classification, called Spectral-Aware CNN with Learnable Spectral Biorthogonal Downsampling Units (LSBDUs) and Depthwise Separable Convolutions. The model replaces conventional pooling layers with wavelet-inspired LSBDUs for improved feature retention. This results in reduced computational overhead through efficient separable convolutions. The research used a balanced dataset of 17,092 images across eight blood cell classes. The techniques, such as stratified data splitting, advanced augmentation, and label smoothing, are included in the training pipeline for improving generalizability. As a result, the model achieves 99.18 % of overall classification accuracy with superior class-wise performance. • Replaces pooling layers with spectral-aware LSBDU blocks for better feature preservation. • Integrates Depthwise Separable Convolutions to reduce parameter count and training cost. • Demonstrates superior generalization across all classes without overfitting.
Open Access: Yes
Ensemble deep learning approach for traffic video analytics in edge computing
Publication Name: Scientific Reports
Publication Date: 2026-12-01
Volume: 16
Issue: 1
Page Range: Unknown
Description:
Video analytics is the new era of computer vision in identifying and classifying objects. Traffic surveillance videos can be analysed to using computer vision to comprehend the road traffic. Monitoring the real-time road traffic is essential to control them. Computer vision helps in identifying the vehicles on the road, but the present techniques either perform the video analysis on the cloud platform or the edge platform. The former introduces more delay in processing while controlling is needed in real-time, the latter is not accurate in estimating the current road traffic. YOLO algorithms are the most notable ones for efficient real-time object detection. To make such object detections feasible in lightweight environments, its tinier version called Tiny YOLO is used. Edge computing is the efficient framework to have its computation done on the edge of the physical layer without the need to move data into the cloud to reduce latency. A novel hybrid model of vehicle detection and classification using Tiny YOLO and YOLOR is constructed at the edge layer. This hybrid model processes the video frames at a higher rate and produces the traffic estimate. The numerical traffic volume is sent to Ensemble Learning in Traffic Video Analytics (ELITVA) which uses F-RNN to make decisions in reducing the traffic flow seamlessly. The experimental results performed on drone dataset captured at road signals show an increase in precision by 13.8%, accuracy by 4.8%, recall by 17.4%, F1 score by 19.9%, and frame rate processing by 12.8% compared to other existing traffic surveillance systems and efficient controlling of road traffic.
Open Access: Yes
Promoting transition towards sustainable air transport systems: A hybrid decision support system for effective national-level performance evaluation
Publication Name: Journal of Air Transport Management
Publication Date: 2026-05-01
Volume: 133
Issue: Unknown
Page Range: Unknown
Description:
Air transport plays a pivotal role in enhancing economic development by supporting trade, tourism, and regional competitiveness. The growing environmental concerns and social expectations have necessitated the transition towards sustainable air transport systems. Sustainable air transport refers to aviation activities that balance environmental, economic, and social objectives, aiming to minimize carbon emissions, promote renewable energy usage, and enhance socio-economic welfare. In this study, a novel multi-criteria decision-making (MCDM)-based decision support system (DSS) is proposed to evaluate the sustainable air transport performance of the European countries. The main objective of this research is to develop a comprehensive and integrative framework for measuring and ranking the sustainable air transport performance of nations. A hybrid method, termed fractional fuzzy–ranking comparison-response to criteria weighting (RANCOM)–response to criteria weighting (RECA)–ranking technique by geometric mean of similarity ratio to optimal solution (RATGOS), is introduced. DSS consists of five main stages: expert-based subjective weighting using fractional fuzzy RANCOM, objective weighting via RECA, aggregation of weights, and final performance ranking through the RATGOS method. The results indicate that Germany ranks highest, while Cyprus has the lowest sustainable air transport performance among the evaluated countries. The criterion “commercial aircraft fleet by age of aircraft” is determined to have the highest importance among the sustainable air transport performance indicators. The study provides a comprehensive, replicable framework for policymakers and stakeholders aiming to monitor and improve sustainable aviation systems. It contributes to the literature by addressing the gap in national-level sustainable air transport performance evaluation.
Open Access: Yes
An interval-valued triangular fuzzy decision analytics model for analyzing surface water quality of urban lakes for sustainable management
Publication Name: Water Reuse
Publication Date: 2025-12-01
Volume: 15
Issue: 4
Page Range: 684-700
Description:
We present a comparative assessment of the surface water quality (SWQ) of two prominent lakes in Udaipur, India: Pichola Lake (PL) and Fateh Sagar Lake (FS), during two distinct seasons, pre-monsoon (PM) and post-monsoon (POM). We measure the physicochemical parameters for assessing the SWQs of PL and FS during the PM and POM seasons. The physicochemical parameters are evaluated (in accordance with the standard specifications) within specified intervals, categorized as excellent, allowable, and unsuitable. For a precise discrimination among the alternatives in a scalable modeling framework while offsetting the uncertainty (imposed due to sample collection and seasonality), the present work uses interval-valued TFNs (IVTFNs). We utilize a hybrid multi-criteria decision-making (MCDM) framework, incorporating the opinion weight criteria method (OWCM) and root assessment method (RAM), to rank the SWQs of the lakes. The OWCM method provides a reliable outcome under imprecision and limited data, while avoiding the oversimplification of diverse alternatives. RAM offers a realistic treatment of contributions by inferior criteria while rewarding superior performance. We observe that FS demonstrates an inseparable SWQ for both the PM and POM seasons. At the same time, PL reports a significant effect of seasonal changes, where PM performance is found to be better.
Open Access: Yes
DOI: 10.2166/wrd.2025.067
Critical impact of automobile industry with advanced decision support system and Aczél-Alsina Hammy mean operators
Publication Name: Scientific Reports
Publication Date: 2026-12-01
Volume: 16
Issue: 1
Page Range: Unknown
Description:
The automobile industry plays a pivotal role in global economic development, technological innovation, and sustainable mobility solutions. It drives advancements in engineering, manufacturing, and smart technologies and influences transportation systems. A decision analysis system in the automobile industry serves as a structured framework to evaluate complex choices involving design, production, supply chain, marketing, and sustainability strategies based on vague human information. To achieve the main goal of this article, we explore the concepts of spherical fuzzy sets (SFSs) for handling uncertainty and vagueness in human judgments. The SFS is a more efficient and broader fuzzy framework that has extensive information about an object. Besides the concepts discussed in the fuzzy framework, we also modify the theory of Hamy mean (HM) models under Aczel Alsina operations. By combining two different theories of Aczel Alsina operations and Hamy mean models, we derive a family of mathematical models, namely spherical fuzzy Aczel Alsina Hamy mean (SFAHM) and spherical fuzzy Aczel Alsina weighted Hamy mean (SFAWHM) operators. Moreover, another generalization of the Dual HM (DHM) models is modified in the form of spherical fuzzy Aczel Alsina DHM (SFADHM) and spherical fuzzy Aczel Alsina weighted DHM (SFAWDHM) operators. Some reliable and appropriate characteristics are also studied to demonstrate the flexibility of the proposed operators. An intelligent decision algorithm of the multi-attribute group decision-making (MAGDM) problem is discussed to resolve real-life applications and a group of expert’s opinions. To see the effectiveness and reliability of newly developed terminologies, we discussed a numerical example to choose desirable alternatives under an automobile industry system. The influence study is also presented here by setting numerous parametric values in the currently discussed methodologies. To showcase the validation and superiority of diagnosed mathematical models, we establish a comparative study to compare the results of invented approaches with the results of existing terminologies.
Open Access: Yes
Hospital Admission Classification of Cardiac Patients Utilizing Metaheuristics-Optimized Two Tier Framework
Publication Name: International Journal of Computational Intelligence Systems
Publication Date: 2026-12-01
Volume: 19
Issue: 1
Page Range: Unknown
Description:
Accurate evaluation of a cardiac patient’s risk at the point of hospital entry is critical for efficient triage and ensuring timely, suitable medical intervention. This study aims to forecast a range of clinical outcomes by leveraging admission data from a cardiac care unit, utilizing a refined and optimized machine learning approach. This research introduces a hybrid architecture that integrates convolutional neural networks (CNNs) with advanced machine learning classifiers, namely light gradient boosting machine (LightGBM) and categorical boosting (CatBoost), further enhanced through metaheuristic optimization techniques to maximize their performance. The proposed two-tiered design organizes feature extraction and final decision modeling into a coherent pipeline tailored for multi-class hospital admission classification. A comprehensive evaluation using a real-world hospital admission dataset demonstrates the framework’s effectiveness on a real-world, publicly available hospital admission dataset, supporting its utility for multi-class cardiac outcome prediction. Three experiments were conducted using publicly available datasets, where the best-performing models achieved a peak classification accuracy of 99.79%. Furthermore, explainable AI techniques were employed to interpret model predictions, offering actionable insights that can guide future data acquisition and strengthen the accurate classification of cardiac patients.
Open Access: Yes
Virtual reality headsets for employee training in enterprises: fuzzy SRP data-driven framework for a comprehensive evaluation
Publication Name: Virtual Reality
Publication Date: 2026-03-01
Volume: 30
Issue: 1
Page Range: Unknown
Description:
Virtual reality (VR) is progressively transforming employee training in companies by offering immersive and engaging learning experiences. Nevertheless, the selection of an appropriate VR headset is vital for optimizing training effectiveness. This paper addresses this issue by proposing a novel hybrid fuzzy multi-criteria decision-making model that integrates the improved fuzzy stepwise weight assessment ratio analysis (IF-SWARA) with the fuzzy simple ranking process (F-SRP). The IF-SWARA methodology is employed to compute the relative weights of the selection criteria for VR headsets utilized in employee training, whereas the newly developed F-SRP is implemented to rank the various VR headsets. By employing the IF-SWARA method, the model offers a more nuanced understanding of criteria weights, thereby reflecting the differing significance of various headset features. The research’s novelties and contributions are as follows: (1) This study is the first to select VR headsets by applying multi-criteria methods. (2) The F-SRP model is developed for the first time in the literature. (3) The introduced F-SRP methodology allows for a comprehensive ranking of the available VR headsets, facilitating informed decision-making. The paramount indicators for selecting VR headset options for training in enterprises consist of technical specifications, comfort and ergonomics, and screen specifications. The results obtained from the fuzzy SRP indicate that the Apple Vision Pro surpasses the other alternatives. Finally, the robustness and applicability of the proposed model are evaluated through an exhaustive sensitivity analysis. This research possesses broader implications for VR training in enterprises by providing a robust and reliable framework, ultimately contributing to the development of more effective and impactful VR training programs.
Open Access: Yes
AN ACCELERATED BENDERS DECOMPOSITION ALGORITHM FOR THE MULTI-ITEM FIXED-CHARGE TRANSPORTATION PROBLEM
Publication Name: Engineering Review
Publication Date: 2025-01-01
Volume: 45
Issue: 2
Page Range: 52-66
Description:
In today’s industrial and service sectors the role of transportation is unavoidable. Due to this importance, an optimized transportation plan with minimum transportation costs can be a favour for the managers. In this study a multi-item fixed-charge transportation problem with capacitated multiple transportation mode is considered. As such problem is of high degree of complexity, we focus on the Benders decomposition approach to solve it. For this aim, first the classical Benders decomposition approach is developed for the problem. This is the first time in the literature that the Benders decomposition algorithm is developed for this problem. In continue, as another novelty, an accelerated benders decomposition algorithm is developed for the problem by adding some valid inequalities to the classical Benders decomposition algorithm. These valid inequalities can effectively influence the performance of the classical Benders decomposition algorithm. Several test problems with various sizes are generated to test the proposed solution approaches. The test problems are solved by the classical branch and bound algorithm and the proposed classical and accelerated Benders decomposition algorithms. According to the obtained results the accelerated Benders decomposition algorithm performs better than others in terms of reporting optimal solution and CPU running time.
Open Access: Yes
DOI: 10.30765/er.2661
Evaluating blockchain-based waste management investments in smart cities using a multi-criteria decision support framework
Publication Name: Scientific Reports
Publication Date: 2026-12-01
Volume: 16
Issue: 1
Page Range: Unknown
Description:
With growing urbanization, there are increasing demands on waste management systems that can be performed in an environmentally friendly way as well as efficiently. Current approaches to managing waste often have issues with efficiency, transparency, and engaging with the public. Blockchain technology has been identified as one potential solution to these problems because it offers several benefits including decentralization, security, and transparency. The selection of the best blockchain-based waste management (BBWM) system is very difficult due to the many different evaluation criteria that may conflict with each other. Therefore this research uses a multi-criteria decision making (MCDM) approach using CIMAS (Criteria Importance Assessment), for determining weights based upon subjective input, and LOPCOW (Logarithmic Percentage Change-Driven Objective Weighing), for determining weights based upon objective data within the MCDM framework. To rank alternatives effectively, an Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN) technique is applied, ensuring a precise evaluation process. The use of T-Spherical Fuzzy Sets (T-SFS) captures all three (membership, non-membership, hesitation degree) and is used to address the variability that exists when making an expert judgment. Some of the key factors include; Technological Feasibility, Operational Costs, Scalability, Data Security, Regulatory Compliance, Environmental Impact. Based on the evaluation criteria, it appears that the Blockchain Enabled Waste Tracking System is the most appropriate alternative due to its high potential for Transparency, Regulatory Compliance and Fraud Prevention. In addition, this research will provide Policymakers, Urban Planners and Investors with a methodical way of making Data Driven Decisions on BBWM Investments.
Open Access: Yes
Mathematical Analysis of Real-Time Data Processing Methods for IoT Applications Based on Hesitant Bipolar Fuzzy Dombi Power Operators
Publication Name: Systems and Soft Computing
Publication Date: 2026-06-01
Volume: 8
Issue: Unknown
Page Range: Unknown
Description:
The rapid growth of Internet of Things (IoT) technologies has made real-time data processing a critical component for efficient monitoring, analysis, and intelligent decision-making in dynamic and large-scale environments. IoT systems continuously generate massive volumes of heterogeneous data that must be processed with minimal latency to ensure timely responses and reliable system performance. Effective real-time data processing enables IoT applications to adapt to changing conditions, enhance operational efficiency, improve safety and reliability, and support time-sensitive services in domains such as smart cities, healthcare monitoring, industrial automation, and intelligent transportation systems. This study presents a comprehensive mathematical framework for the analysis of real-time data processing methods for IoT applications based on hesitant bipolar fuzzy (HBF) Dombi power operators. The proposed model is designed to effectively capture uncertainty, hesitation, and bipolar information that naturally arise in real-world IoT environments due to incomplete, imprecise, and conflicting data sources. By incorporating a multi-criteria decision-making (MCDM) approach, multiple real-time data processing techniques are systematically evaluated and prioritized with respect to several performance-related attributes. The proposed HBF Dombi power-based framework offers a reliable and transparent mechanism for comparing competing real-time data processing strategies and selecting the most suitable method for specific IoT scenarios. The results indicate that the proposed approach improves decision accuracy and supports better alignment between data processing methods and the complex operational requirements of modern IoT systems. This work contributes both theoretical insights and practical guidance for the design and evaluation of efficient, adaptive, and intelligent real-time IoT data processing architectures.
Open Access: Yes
Selection of underground hydrogen storage systems using a novel fuzzy model
Publication Name: Energy Conversion and Management
Publication Date: 2026-03-15
Volume: 352
Issue: Unknown
Page Range: Unknown
Description:
Storing hydrogen resources underground can accelerate the transition to renewable energy, facilitate energy supply security, and the adoption and expansion of hydrogen energy, a clean energy source. The selection of sustainable underground hydrogen storage systems is a critical research topic for addressing environmental issues caused using fossil fuels. However, decision-makers still lack a consensus-based and sustainability-oriented framework that can comparatively evaluate alternative underground hydrogen storage geological formations under economic, environmental, social, and technical uncertainties, which constitutes a critical barrier to large-scale hydrogen deployment. This issue has become more prominent as fossil-based fuel reserves are gradually decreasing worldwide. In contrast, researchers and practitioners lack a consensus on which underground storage method is most suitable for economical, safe, and efficient hydrogen storage. If this problem is not addressed correctly and reasonable solutions are not obtained, continued dependence on fossil fuels may persist. Alternatively, other renewable energy sources with relatively lower efficiency and performance may be adopted. In both cases, significant delays in achieving the global sustainability goal are likely to occur. We propose an integrated fuzzy decision-making framework (F-WENSLO & Dombi-Bonferroni & F-ARTASI) to address this selection problem under uncertainty. The proposed framework integrates fuzzy WENSLO (Weights by ENvelope and SLOpe) for robust sustainability-based criteria weighting, the Dombi–Bonferroni aggregation operator to model interdependencies among criteria explicitly, and the fuzzy ARTASI (Alternative Ranking Technique based on Adaptive Standardized Intervals) method to provide flexible and stable ranking of geological alternatives beyond rigid distance-based approaches. Key advantages of the proposed model include producing reliable and consistent solutions that accurately reflect real-world conditions for selecting sustainable underground hydrogen storage systems. The results revealed that C14 (job creation and employment opportunities) (0.0603) is the most influential criterion in selecting the most suitable storage system. In addition, salt caverns with an Ωi of 10,5167 have achieved the highest score, placing them in the first position, and it is the most suitable and advantageous underground hydrogen storage option. The suggested decision-making tool can yield reliable and robust solutions in real-world conditions, enabling the planning of infrastructure design for hydrogen energy systems that incorporate sustainability dimensions. In that regard, the developed model possesses the characteristics of an efficient and practical roadmap that can guide policymakers and decision-makers in transitioning from fossil-based energy sources to renewable energy sources. It has been implemented to evaluate underground geological formations that could facilitate the storage of hydrogen energy underground, serving as a case study. The reliability and robustness of this tool have been verified through extensive validation tests.
Open Access: Yes
Modeling Hepatitis B and Alcohol Effects on Liver Cirrhosis Progression
Publication Name: CMES Computer Modeling in Engineering and Sciences
Publication Date: 2026-01-01
Volume: 146
Issue: 1
Page Range: Unknown
Description:
Hepatitis B Virus (HBV) infection and heavy alcohol consumption are the two primary pathogenic causes of liver cirrhosis. In this paper, we proposed a deterministic mathematical model and a logistic equation to investigate the dynamics of liver cirrhosis progression as well as to explain the implications of variations in alcohol consumption on chronic hepatitis B patients, respectively. The intricate interactions between liver cirrhosis, recovery, and treatment dynamics are captured by the model. This study aims to show that alcohol consumption by Hepatitis B-infected individuals accelerates liver cirrhosis progression while treatment of acutely infected individuals reduces it. We proved that a unique solution of the proposed model exists, which is positive and bounded. Using the next-generation matrix approach, two basic reproductive numbers RA0 and RAmax are calculated to identify future recurrence. The equilibrium points are calculated, and both equilibria are proved locally and globally asymptotically stable when R0 is below and above one, respectively. It is shown that bifurcation exists at R0 = 1 and a detailed proof for forward bifurcation is given. Furthermore, we performed the sensitivity analysis of the model parameters on R0 . For the confirmation of analytical work, we performed numerical simulations, and the results indicate that the treatment and the inhibitory effects reduce the risk of developing liver cirrhosis in individuals, while heavy alcohol consumption accelerates markedly the liver cirrhosis progression in patients with chronic hepatitis B.
Open Access: Yes
Hybrid Pythagorean Fuzzy Decision-Making Framework for Sustainable Urban Planning under Uncertainty
Publication Name: CMES Computer Modeling in Engineering and Sciences
Publication Date: 2026-01-01
Volume: 146
Issue: 1
Page Range: Unknown
Description:
Environmental problems are intensifying due to the rapid growth of the population, industry, and urban infrastructure. This expansion has resulted in increased air and water pollution, intensified urban heat island effects, and greater runoff from parks and other green spaces. Addressing these challenges requires prioritizing green infrastructure and other sustainable urban development strategies. This study introduces a novel Integrated Decision Support System that combines Pythagorean Fuzzy Sets with the Advanced Alternative Ranking Order Method allowing for Two-Step Normalization (AAROM-TN), enhanced by a dual weighting strategy. The weighting approach integrates the Criteria Importance Through Intercriteria Correlation (CRITIC) method with the Criteria Importance through Means and Standard Deviation (CIMAS) technique. The originality of the proposed framework lies in its ability to objectively quantify criteria importance using CRITIC, incorporate decision-makers' preferences through CIMAS, and capture the uncertainty and hesitation inherent in human judgment via Pythagorean Fuzzy Sets. A case study evaluating green infrastructure alternatives in metropolitan regions demonstrates the applicability and effectiveness of the framework. A sensitivity analysis is conducted to examine how variations in criteria weights affect the rankings and to evaluate the robustness of the results. Furthermore, a comparative analysis highlights the practical and financial implications of each alternative by assessing their respective strengths and weaknesses.
Open Access: Yes
Low-carbon agricultural practices enhance climate resilience and food security in India
Publication Name: Discover Sustainability
Publication Date: 2026-12-01
Volume: 7
Issue: 1
Page Range: Unknown
Description:
Low-carbon agricultural (LCA) practices, including nutrient, water, and soil management, present viable strategies for mitigating greenhouse gas (GHG) emissions while enhancing agricultural productivity. However, their long-term impacts on food security and emission reduction at the national scale require further investigation. This study employs scenario-based analysis to assess the role of LCA in reducing carbon dioxide, nitrous oxide, and methane emissions while evaluating its effects on food production, accessibility, and availability in India. By conceptualizing LCA as a baseline scenario, the study examines the influence of technology adoption, government policies, and sustainable agricultural practices in enhancing food security and mitigating climate change. A systematic literature review, following the PRISMA protocol, was conducted using keyword co-occurrence analysis from major global databases, including Scopus, ScienceDirect, Web of Science, and government and organizational sources. The findings indicate that efficient resource and nutrient management significantly strengthen food security while reducing annual GHG emissions, supporting India’s progress toward food self-sufficiency and climate resilience. These insights provide a foundation for strengthening national and global food policies and climate mitigation strategies, aligning with multiple Sustainable Development Goals (SDGs), including SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), SDG 12 (Responsible Consumption and Production), SDG 13 (Climate Action), SDG 15 (Life on Land), and SDG 17 (Partnerships for the Goals). This study underscores the critical role of LCA in integrating food security with environmental sustainability, offering a policy-driven approach to climate adaptation and sustainable agricultural development in India.
Open Access: Yes
Measuring promotional video performance through eye-tracking and cognitive evaluation: A Fermatean fuzzy decision analytics approach
Publication Name: Engineering Applications of Artificial Intelligence
Publication Date: 2026-04-15
Volume: 170
Issue: Unknown
Page Range: Unknown
Description:
Neuromarketing techniques are increasingly employed to measure emotional responses through brainwaves, eye movements, and facial expressions. The primary motivation of this study is to develop a decision support system capable of evaluating promotional video performance levels based on both eye-tracking data and cognitive assessments. The core objective is to propose a hybrid method that simultaneously integrates neuromarketing insights and cognitive evaluations. To this end, a Fermatean fuzzy (FF)−Hamacher−simple weight calculation (SIWEC)−method based on the removal effects of criteria (MEREC)−alternative ranking using two-step logarithmic normalization (ARLON) decision analytics model is developed and implemented. The FF−SIWEC method is employed to determine subjective criterion weights based on expert judgments, while the FF−MEREC method is used to compute objective weights by analyzing both eye-tracking and qualitative evaluations from viewers. Additionally, the Fermatean fuzzy Hamacher weighted aggregation operator ensures precise aggregation of audience evaluations. The FF−ARLON method is applied to obtain final rankings of promotional videos. A real-world case study is conducted to test the applicability of the proposed method, involving six automobile brands and 10 audiences. Eye-tracking analyses are conducted while audiences view the promotional videos, followed by expert and audience evaluations of seven qualitative and six eye-tracking criteria. Among qualitative criteria, “level of emotional impact” is found to be the most significant, while “saccadic direction” emerges as the most important eye-tracking criterion. The promotional video for the Mercedes brand demonstrates the highest overall performance. This study contributes to the literature by proposing a reliable and consistent hybrid model for evaluating promotional video performance.
Open Access: Yes
Analysis and management of climate change incidents spread within the environment under coastal lives: Modeling and chaos control
Publication Name: Results in Control and Optimization
Publication Date: 2026-03-01
Volume: 22
Issue: Unknown
Page Range: Unknown
Description:
Examining the model of climate change by analyzing how changes in climate-related incidents spread within the environment, particularly in coastal areas, as a result of predictions, is the main goal of this study. Following some measurements of impact rates for various variables, a mathematical model is developed using the hypothesis of a healthy environment to investigate the rates of climate change affecting coastal communities. In addition to studying the model equilibrium points, the next generation method is used to determine the models reproductive number to climate incidents spread within the environment. To determine the most sensitive factors and look at how changes in the pace of change under various conditions affect coastal life, a sensitivity analysis was created. Both qualitative and quantitative analyses are performed on a proposed model, with particular focus on existence, boundedness, positivity, and unique solutions, which are key characteristics of the developed model. At endemic sites, the model's local stability is confirmed both theoretically and statistically. The Lyapunov derivative by endemic point of the model is used to investigate the worldwide stability of the model. Chaos control is also used to observe the chaotic behavior of the climate change. A two-step method, Lagrange polynomials, is applied in numerical simulations to investigate the effect of the fractional operator on the generalized form of the power law kernel for ongoing surveillance of climate change under coastal lives. The simulations show how different parameters affect the changes in climate incidents spread within the environment under coastal lives. Simulations have been developed to simulate the effects and behavior of climate change brought on by both natural and human activity, as well as to implement various environmental health initiatives. This type of research will be helpful in figuring out how climate change spreads and in developing future management plans for coastal lives, based on our verified results for various strategies.
Open Access: Yes
Positive Impact of Waste Management Strategies and Decision Analysis with Intuitionistic Fuzzy Sugeno-Weber Aggregation Operators
Publication Name: Boletim Da Sociedade Paranaense De Matematica
Publication Date: 2025-08-13
Volume: 43
Issue: 3
Page Range: Unknown
Description:
Waste management is a crucial and significant subject that has gained much attention globally because it has several environmental, social, financial and economic implications. Solid waste management is a very challenging task for clean urban and rural societies. We studied some reliable strategies for handling the waste materials and garbage produced by people. To serve this purpose, an intuitionistic fuzzy set (IFS) is a well-known model used for modeling and processing unpredictable information and providing accurate approximated results in the decision-making process. Power average operators allow the interrelationship of the input arguments and deal with uncertain information in complicated situations. This article expresses Sugeno-weber triangular norms under intuitionistic fuzzy (IF) information. We developed a class of new aggregation operators, including intuitionistic fuzzy Sugeno-Weber power-weighted average (IFSWPWA) and intuitionistic fuzzy Sugeno-Weber power-weighted geometric (IFSWPWG) operators. It is observed that both the newly proposed operators satisfy the properties of aggregation. The multi-criteria decision-making (MCDM) problem is proposed to evaluate real-life applications and numerical examples. An experimental case study under the system of waste materials is considered in the article to reveal the intensity and applicability of derived approaches. The comparison analysis and sensitivity analysis show the significance of our proposed work.
Open Access: Yes
DOI: 10.5269/bspm.79085
Intuitionistic Fuzzy Best-Worst Method for Multi-Criteria Decision Making with Application in Health Care Resource Allocation
Publication Name: International Journal of Analysis and Applications
Publication Date: 2026-01-01
Volume: 24
Issue: Unknown
Page Range: Unknown
Description:
In the health care industry, decision-making is critical for determining the most efficient use of limited resources. Multi-criteria decision-making is a significant area that has been used to solve complex problems. To construct an accurate, adaptable, and sustainable framework for decision-making, an intuitionistic fuzzy best-worst method for multi-criteria decision-making in healthcare resource allocation is being developed. To understand the resource allocation mechanisms in different hospitals, the proposed methods employ a pairwise comparison of seven main criteria: infrastructure, consultancy time, paramedics, hospital stay, healthcare resource allocation, healthcare professionals’ satisfaction, and improvements in resource allocation. The weights calculated from the intuitionistic fuzzy best-worst method indicate that health professional satisfaction is the best criterion, whereas the consultancy time is the worst. The goal of this approach is to effectively handle the inherent ambiguity, complexity, and uncertainty that define problems with healthcare resource allocation. This methodology has a wide range of applications, including: hospital resource management, prioritizing patient care during peak times or emergencies such as pandemics, budgeting and financial planning, evaluating the cost-effectiveness of new treatments or technologies, public health planning, planning and executing community health interventions, strategic planning, and policy making.
Open Access: Yes
Multi robot task assignment with decision analysis and circular q-Rung orthopair fuzzy Schweizer-Sklar T-norms
Publication Name: Journal of Umm Al Qura University for Applied Sciences
Publication Date: 2026-01-01
Volume: Unknown
Issue: Unknown
Page Range: Unknown
Description:
A multi-robotic task assignment and decision analysis system refers to an advanced framework in robotics and artificial intelligence where multiple robots are coordinated to perform a set of tasks efficiently and intelligently. This article designs innovative approaches to fix uncertainty during task allocation in a multi-robotic system under a hybrid fuzzy framework and decision-making models. To achieve this goal, we expose a modified theory of circular q-rung orthopair fuzzy set (Crq-ROFS), which is a broader framework of intuitionistic fuzzy sets and q-rung orthopair fuzzy sets. We formulated feasible operations of Schweizer-Sklar t-norm and t-conorm in light of circular information about q-rug orthopair fuzzy (Crq-ROF). We also delved into a family of mathematical approaches of Schweizer-Sklar t-norm and t-conorm, namely, Crq-ROF Schweizer-Sklar weighted average (Crq-ROFSSWA) and Crq-ROF Schweizer-Sklar weighted geometric (Crq-ROFSSWG) operators with dominant propositions. The theory of the multi-attribute decision-making (MADM) problem offers authentic and reliable solutions by aggregating human judgment. An experimental case study discussed evaluating an ideal solution under consideration of multi-criteria or attribute information. A comparison method is conducted to showcase the reliability and effectiveness of the pioneering approaches with existing approaches.
Open Access: Yes
Strategic selection of electric vehicles in the context of smart city development in Albania using the fuzzy MCDM methods
Publication Name: Clean Energy Science and Technology
Publication Date: 2026-01-01
Volume: 4
Issue: 1
Page Range: Unknown
Description:
The automotive industry is undergoing a significant transformation towards electric vehicles (EVs) with the main goal of reducing greenhouse gas emissions and for a sustainable and green environment. Different types of EVs are introduced every day in the market where selecting an optimal vehicle for purchase constitutes a complex decision-making. Therefore, the purpose of this research was to evaluate EVs in Albania using multi-criteria decision-making methods (MCDM). A total of 12 vehicles were analyzed based on 4 main criteria and 12 sub-criteria. The fuzzy Logarithm Methodology of Additive Weights (LMAW) method was applied to find the weights of the main criteria while the fuzzy Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) method was applied to find the weights of the sub-criteria. For the EV ranking, the fuzzy Ranking of Alternatives with Weights of Criterion (RAWEC) method was applied. The findings showed that the most important criteria are the technical criteria and the Auto 11 vehicle showed the best results. The combination of Fuzzy LMAW-Fuzzy LOPCOW-Fuzzy RAWEC methods also constitutes the novelty of this research, which has not been applied before in this field. The contribution of this research consists in providing a comprehensive set of selection criteria to choose the best alternative of the EV fleet in Albania. Furthermore, the contribution of this research was the application of a hybrid methodology in the evaluation and selection of an electric vehicle as an ongoing choice faced by vehicle buyers.
Open Access: Yes
DOI: 10.18686/cest548
ReGAIN: a reinforcement-enhanced generative AI framework for intelligent intrusion detection in IoT networks
Publication Name: Complex and Intelligent Systems
Publication Date: 2026-04-01
Volume: 12
Issue: 4
Page Range: Unknown
Description:
The advent of the Internet of Things (IoT) enables billions of devices in wide-ranging domains such as healthcare, industry, and smart cities to interconnect with each other, but these connections make the network vulnerable to advanced cyber threats too. Current intrusion detection methods have failed to provide effective detection capabilities mainly because of issues such as extremely imbalanced data distributions, low classification accuracy, or static and manually tuned hyperparameters that do not generalize well in dynamic IoT settings. These challenges are exacerbated by unique IoT constraints, including limited device resources and dynamic attack patterns, which further complicate effective detection. To address these challenges, in this study we present a Reinforcement-enhanced Generative Artificial Intelligence (ReGAIN) framework for intelligent intrusion detection in IoT networks. In this approach, we use a generative autoencoder for data balancing to generate realistic minority class instances in the latent feature space, and meanwhile to obtain stable and unbiased learning of the model. This paper introduces a novel Pointer-Attention Dual Network (PAD-Net) that employs a Dual Attention Network (DANet) and a Pointer Network (PtrNet) to enhance spatial attention and inter-feature relationships. We also propose Reinforcement-enhanced PAD-Net (RePAD-Net), which leverages reinforcement learning to automatically optimize key hyperparameters at each training step, further enhancing generalization ability and robustness. The intrusion detection task in this study is a multi-class classification problem, where different types of attacks are distinguished from each other. Experimental results demonstrate that PAD-Net and RePAD-Net achieve notable improvements of 3.79% and 8.79% in accuracy, 3.79% and 8.78% in recall, 2.79% and 9.01% in F1-score, 3.79% and 8.83% in Mathews correlation coefficient, and 3.94% and 9.11% in Cohen’s Kappa, respectively, along with significant reductions in log loss of 47.42% and 70.96% and hamming loss of 24.33% and 56.37% compared with baseline models such as naive bayes, gradient boosting, densely connected network, long short term memory, hybrid models, DANet and PtrNet. Additionally, 10-fold cross validation is applied to validate the results of proposed models. These findings confirm that our proposed ReGAIN framework, which is able to alleviate data imbalance and improve learning generalization, can dramatically enhance the reliability of detection performance under complex IoT intrusion environments.
Open Access: Yes
Complex intuitionistic fuzzy distance measures with hesitance value and their applications in decision making
Publication Name: Physica Scripta
Publication Date: 2026-01-16
Volume: 101
Issue: 2
Page Range: Unknown
Description:
In applications requiring uncertain, imprecise, and multi-dimensional data, where traditional distance measures frequently fall short of capturing the full complexity of interactions among elements, a distance measure for complex intuitionistic fuzzy sets (DMCIFSs) becomes essential. Although DMCIFSs have been developed, most of them do not account for the hesitation degree, which is crucial for capturing ambiguity and uncertainty in human reasoning. As extensions of the normalized Hamming and Euclidean distance measures, this work proposes two new measures namely the Hesitance DMCIFSs (HDMCIFSs) and the Euclidean Hesitance DMCIFSs (EHDMCIFSs). These newly proposed measures provide a more comprehensive framework for modeling uncertainty by explicitly incorporating the hesitancy component. In addition to the proposed measures, several fundamental procedures and theoretical results are also presented. Furthermore, a novel decision-making method utilizing these distance measures is developed and applied to multi-criteria decision-making (MCDM) problems. The effectiveness of the proposed methods is demonstrated through a comparative study, highlighting their potential for improved sensitivity and accuracy in practical decision-making scenarios.
Open Access: Yes
NET ZERO TRANSITION TOWARDS DECARBONIZATION IN CONTEXT OF ENERGY SECTOR
Publication Name: Economics Innovative and Economics Research Journal
Publication Date: 2026-01-01
Volume: 14
Issue: 1
Page Range: 483-514
Description:
The study provides an identification and analysis of potential enablers that facilitate transition towards net zero in the energy sector through Multi Criteria Decision-Making (MCDM) framework. The identified enablers and causal relationships between them in terms of decarbonization initiatives are studied using the DEMATEL method and combining trapezoidal fuzzy numbers (TFNs). The research design involves an overarching review of thirteen potential enablers to net zero transition within the energy sector, in order of their impact and causality. Top-ranked enablers that would have the greatest impact in achieving the energy transition were carbon pricing mechanisms, waste-to-energy conversion, decentralized energy systems and circular procurement policies. The research indicates that the enablers show causal pathways that are interconnected and can take place as both causes and effects in the decarbonization framework. Application of DEMATEL method using TFNs increases the strength of causal relationship derivation. The study adds to the literature on enabling net zero transition in energy and highlights the importance of a conceptual approach involving a combination of policy, technology and principles of the circular economy. Such lessons can guide policy makers, industry players and academics in planning and speeding up the process to sustainable energy systems and world climate targets.
Open Access: Yes
A novel hybrid neutrosophic-fuzzy-uncertain data envelopment analysis model for assessment of wind farm locations
Publication Name: Energy Nexus
Publication Date: 2026-06-01
Volume: 22
Issue: Unknown
Page Range: Unknown
Description:
Renewable energy resources have got much attention in recent years. Because of climate change in the world, the countries try to develop renewable energy resources. Wind farms are renewable energy resources that can produce electricity with no negative effect on climate change. In this study, as an important topic, the location selection problem of wind farms is considered as a real case study. For the first time, a multi-criteria wind farm location selection problem with neutrosophic fuzzy and uncertain criteria at the same time is developed. As the set of criteria consists of both input and output criteria, we develop a novel hybrid neutrosophic-fuzzy-uncertain scheme of BCC DEA model for the first time to solve the problem. As solution approach, a chance-constrained programming approach based on possibility measure of the neutrosophic fuzzy constraints of the model also for the first time is proposed in this study. An extensive computational study on the case study by the proposed approach is performed. The candidate locations of the case study are prioritized where the location of Birjand city with score of 1.1656 is selected as the best location. A sensitivity analysis on the confidence levels of the chance-constrained programming approach is performed and also the obtained results are compared to the approaches of the literature.
Open Access: Yes
A novel hybrid decision support methodology for data-driven comparative analysis of EU macroeconomic performance
Publication Name: Journal of Applied Economics
Publication Date: 2026-01-01
Volume: 29
Issue: 1
Page Range: Unknown
Description:
This study aims to evaluate the macroeconomic performance of 24 European Union (EU) member countries for the period 2014–2023 using the data-driven Multi-Criteria Decision Making (MCDM) approach. In this context, an integrated weighting methodology combining Entropy and CRITIC techniques is applied together with Adaptive Standardized Intervals Based Alternative Ranking Technique (ARTASI) to ensure objectivity, stability and sensitivity in the analysis. In addition, in order to provide a comparative assessment of subjective and objective weightings in the study, opinions received from eight academicians who are experts in their fields are carried out with the Analytical Hierarchy Process (AHP) approach. Evaluating countries across eight key indicators, the results demonstrate that integrating both weighting methods enhances the robustness of the rankings Malta, the Netherlands, Denmark, the Czech Republic and Ireland generally show strong economic stability over the period considered, while Greece, Italy, Spain, France and Romania show the weakest weak performances.
Open Access: Yes
A maturity model to assess startups toward industry 4.0: Combined compromise solution approach
Publication Name: Sustainable Futures
Publication Date: 2026-06-01
Volume: 11
Issue: Unknown
Page Range: Unknown
Description:
The fourth industrial revolution is the integration of new digital industrial technologies aimed at connecting and enabling interaction between humans, machines, and components, transforming production systems into fully automated and integrated equipment that communicate with each other. In this paper, we propose a new maturity model for Industry 4.0 (I4.0) startups using the combined compromise solution (CoCoSo) decision-making algorithm. This model assists companies seeking to transition their business towards I4.0 and can serve as a guiding procedure for systems on their path to full I4.0 implementation. First, we compile a list of key I4.0 dimensions from the literature and previous studies. Then, we gather opinions from industrial and academic experts to select the most relevant and important indicators for analyzing the maturity level of startups. We use the CoCoSo decision-making algorithm to prioritize the I4.0 dimensions and design the model elements. Three main sustainability dimensions are used as decision criteria with equal weight when prioritizing the I4.0 sub-dimensions. To evaluate the applicability of the model, we assess three Iranian startups operating at the Science and Technology Park using the proposed model and present the radar graph of their maturity level in each dimension. The proposed model can be readily used by startups to assess themselves, identify their strengths and weaknesses, better define improvement projects, and allocate their resources. To our knowledge, there is no similar assessment model in the literature that can be used by startups due to their innovative nature.
Open Access: Yes
Correlation coefficients on normal wiggly dual hesitant fuzzy sets: an application in the selection of real estate agents
Publication Name: Peerj Computer Science
Publication Date: 2025-01-01
Volume: 11
Issue: Unknown
Page Range: Unknown
Description:
Decision makers (DMs) continually demonstrate shortcomings in their approaches to analyzing information through fuzzy systems; nevertheless, a model that integrates many dimensions of uncertainty is generally substantial. Normal wiggly dual hesitant fuzzy sets (NWDHFSs) incorporate a range of DMs' preferences for membership grades (MGs) and non-membership grades (NMGs). For complicated and multifaceted problems, one can apply the dynamic framework of NWDHFSs. To illustrate the relationship between NWDHFSs, correlation coefficients (CCs) on NWDHFSs, as well as weighted CCs on NWDHFSs, are presented in this work. These CCs are built up using means of values in hesitant fuzzy elements of NWDHFSs. Some fundamental axioms and thresholds of CCs on NWDHFSs are examined. A multi-criteria decision-making (MCDM) technique and associated algorithms based on these CCs are introduced. Because of the competitive real estate market, choosing a real estate agent is a challenging task for organizations. Through the consideration of a real estate case study, we select an appropriate real estate agent for a real estate firm utilizing proposed CCs on NWDHFSs. We examine the methodologies and outcomes of our approach to previous strategies.
Open Access: Yes
Linguistic Linear Diophantine Fuzzy Sugeno Border Approximation Area Comparison: Application in Green Supply Chain Management
Publication Name: Journal of Fuzzy Extension and Applications
Publication Date: 2026-12-01
Volume: 7
Issue: 1
Page Range: 225-246
Description:
The Linguistic generalzied Fuzzy Set (FS) is more efficient and effective for depicting awkward and uncertain data compared to existing models. In this manuscript, we describe the Sugeno-Weber laws for linguistic generalzied fuzzy information. Because these operational laws will help us in the construction of the “power aggregation operators” for linguistic Linear Diophantine Fuzzy Sets (LDFSs), called “Power Averaging (PA) operator”, “Power Weighted Averaging (PWA)”, “Power Geometric (PG)”, and “Power Weighted Geometric (PWG)” for linguistic linear Diophantine fuzzy values. These models can help us aggregate the collection of data into a singleton set very easily. Additionally, we investigate the model of the multi-attributive border approximation area comparison technique for derived operators to enhance the effectiveness of the proposed theory. The problem of supply management is used for the integration of environmentally friendly procedures into supply chain management techniques, where the model of sustainable sourcing, eco-design, waste management, energy efficiency, transportation, and collaboration are the major parts of the considered theory. For this, we illustrate some numerical problems for evaluating the problem of supply chain theory by using the proposed models. Finally, we deliberate on the power and strength of the suggested models by comparing the value of the proposed and existing models.
Open Access: Yes
Induced OWA Operators in Neutrosophic Environment Applied in the Economic Assessment of Southeast Asian Countries
Publication Name: Operations Research Forum
Publication Date: 2026-06-01
Volume: 7
Issue: 2
Page Range: Unknown
Description:
The ordered weighted averaging (OWA) operator is a fundamental tool in decision-making processes, particularly under conditions of uncertainty, by aggregating inputs through a reordering mechanism based on predefined weights. Despite its utility, the classical OWA operator is limited in addressing complex decision scenarios characterized by uncertainty, indeterminacy, and inconsistency. This study introduces two innovative aggregation operators, the induced ordered weighted averaging operator under a neutrosophic environment (IOWAN) and its generalized form (GIOWAN), which integrate the ordering flexibility of induced OWA with the expressive power of neutrosophic sets. The proposed operators advance existing models by (i) enabling simultaneous aggregation of truth, indeterminacy, and falsity information, (ii) incorporating application-driven inducing functions for dynamic ordering, and (iii) offering a unified framework encompassing arithmetic, geometric, harmonic, quadratic, and extreme-case induced operators. We formally define the operators, establish their mathematical properties, and present several novel extensions, including interval-valued, bipolar, probabilistic, entropy-based, and time-dynamic neutrosophic versions. An illustrative case study of the economic assessment of Southeast Asian countries was performed using a methodology based on (GIOWAN) Operators and results show that rankings align with real-world economic data. Comparative analyses highlight its superior performance in modeling intricate decision dynamics. The proposed algorithms are effective in a group decision-making environment within uncertain domains, solving problems of uncertainty, complexity, and multidimensional information, including sustainable development, policy formulation, and healthcare decision analysis.
Open Access: Yes
Deep-learning based adaptive fusion of CC and MLO views for improved mammographic cancer diagnosis
Publication Name: Methodsx
Publication Date: 2026-06-01
Volume: 16
Issue: Unknown
Page Range: Unknown
Description:
Breast cancer remains the most prevalent malignancy among women worldwide. The timely detection of this cancer type is critical for improving survival outcomes. Despite advancements, mammogram classification using deep learning strategies still faces challenges. These include inter-view feature inconsistency, loss of diagnostic details, and limited interpretability. In order to address these issues, MammoFusion-Net, a dual-branch deep learning framework, is proposed for mammogram-based breast cancer classification. Using residual convolutional streams, the framework processes craniocaudal (CC) and mediolateral oblique (MLO) views independently. This supports preservation of view-specific anatomical information. In the proposed framework, a Gates Cross-View Fusion mechanism adaptively integrates features across views. As a result of experimental analysis, the proposed framework achieved 92.116 % (VinDr-Mammo dataset) and 95.556 % (INBreast dataset) of improved classification performance.•Employs a dual-branch architecture to independently process CC and MLO views using residual convolutional streams.•Integrates Gated Cross-View Fusion and attention mechanisms adaptively and refines multi-view features for stronger discrimination.•Demonstrates the explainability of the model through Grad-CAM visualizations that highlight lesion-relevant regions.
Open Access: Yes
AI-Driven Stacked Ensemble Intelligence for Robust Link Quality Classification and Adaptive Resource Management in Satellite-Terrestrial Integrated Networks
Publication Name: IEEE Open Journal of the Communications Society
Publication Date: 2026-01-01
Volume: 7
Issue: Unknown
Page Range: 4899-4913
Description:
The Satellite-Terrestrial Integrated Networks (STIN) were emerged as a key architectural pattern for attaining seamless, global and resilient wireless connectivity by adding extensive coverage of satellite systems with the high capacity and low latency of terrestrial networks. In spite of their advantages, STINs face significant challenges arising from heterogeneous link qualities, dynamic network topologies, long propagation delay and highly variable channel conditions which may complicate reliable and adaptable communication. The accurate and timely assessment of link quality is essential to enable effective resource management, adaptive modulation and coding and robust network control in space-ground integrated environments. In this study, AI-based link quality classification model for STINs based on a stacked ensemble learning architecture. This model combines multiple lightweight machine learning classifiers and a meta-level learner to capture complex non-linear relationships among satellite orbital parameters, spatial characteristics, and link dynamics. The framework categorizes satellite-terrestrial links into three operational states as Good, Moderate and Poor, which provides actionable intelligence for cross-layer resource allocation and adaptive communication strategies. The extensive experimental evaluation demonstrates that the proposed work attains 96.79% classification accuracy and 96.88% macro averaged F1-score where Decision tree as 85% and Machine Learning Based attain 88%. This indicates highly balanced and robust performance across all link classes. The confusion matrix analysis reveals that the misclassification occurs only between adjacent link quality states with no critical misclassifications between good and poor links. This ensures high reliability for operational decision-making. When compared to single-model baselines, the proposed approach increases prediction stability and robustness under heterogeneous and dynamic STIN conditions. The results confirmed the machine learning-assisted link quality intelligence can serve as a practical and efficient enabler for dynamic resource management in STIN. The model is computationally effective, scalable and readily deployable within next-generation STIN control planes which supports reliable communication for broadband access, emergency services and IoT applications.
Open Access: Yes
Coherent manipulation of birefringent superluminal bright solitons in a chiral medium
Publication Name: JVC Journal of Vibration and Control
Publication Date: 2026-01-01
Volume: Unknown
Issue: Unknown
Page Range: Unknown
Description:
The superluminal solitonic propagation behavior of left- and right-circularly polarized (LCP/RCP) light beams is investigated in a chiral medium. RCP and LCP beam absorption exhibits super-Gaussian-type peak behavior with spatial coordinate fluctuation around the origin. The RCP beam’s subluminal phase velocity is vp(+)=c/nr(+)[jls-end-space/], and its refractive index is 5.47. On the other hand, the LCP beam’s superluminal phase velocity is vp(−)=c/nr(−)[jls-end-space/], and its refractive index is −3.65. Both LCP and RCP beams propagate at superluminal group speeds in the medium. The RCP and LCP beams’ maximum group index values are determined to be Ng(+)=−33522 and Ng(−)=−1305[jls-end-space/]. The group velocities that correspond to this are vg(+)=−c/33522 and vg(−)=−c/1305[jls-end-space/]. With the position of greater nonlinearity and periodicity changing with forward time flowing, the RCP and LCP pulses E(r,t)(+) and E(r,t)(−) exhibit superluminal-peaked soliton characteristics. With increasing forward time, the LCP pulse intensity shifts to a positive position while the RCP pulse intensity shifts to a negative position. The bright superluminal solitonic intensities are modified and controlled. For both polarized light beams in a chiral atomic medium. The obtained results may be useful for soliton radar technology and time cloak equipment to reduce information hacking from outside hackers.
Open Access: Yes
Building safe organisations: using machine learning to decode safety habits of blue-collar workers in the construction industry
Publication Name: Engineering Management in Production and Services
Publication Date: 2026-03-01
Volume: 18
Issue: 1
Page Range: 42-59
Description:
This study aims to provide a framework for categorising safety behaviours of construction workers, recognising the importance of employee safety in the competitive business environment. Employee safety is crucial to overall efficiency, productivity, and well-being, and the study seeks to contribute to understanding and managing workplace safety in the construction industry. This study utilises machine learning (ML) algorithms, like logistic regression, support vector machine, and decision trees, to develop a categorisation framework for the safety behaviours of construction workers. The framework is validated using frequent safety behaviours observed in a random sample of construction professionals. The study finds that workplace safety behaviours (WSB) are primarily influenced by supervisor support, reckless habits, and safety motivation. Limiting workplace accidents, enforcing safety laws, properly documenting safety processes, and organising sessions to educate staff are identified as critical sub-factors. Advancements in technology have resulted in significant improvements across construction organisations in allied domains. Additional considerations include education, preempting the possibility of accidents in different workplace situations, and enforcing strong disciplinary measures. The framework proposed can serve as a valuable tool for organisations to tailor safety interventions. By recognising the diverse influences on safety behaviours, companies can implement targeted measures to address specific root causes of unsafe practices. The practical implications of these findings for safety management in the construction industry are noteworthy.
Open Access: Yes
Hybrid NLP-based speech augmentation with explainable AI approach for enhancing reliability and explainability in Human-Robot Interaction
Publication Name: ICT Express
Publication Date: 2026-01-01
Volume: Unknown
Issue: Unknown
Page Range: Unknown
Description:
Ensuring task safety in Human-Robot Interaction (HRI) environments is a critical requirement for reliable and trustworthy robotic systems. AI can be used effectively to estimate robot task safety. However, existing systems suffer from limited data availability and class imbalance, resulting in inaccurate detection of unsafe events. To address these issues, a hybrid speech data augmentation approach is proposed, which combines acoustic and linguistic approaches to train the ML models effectively. The experimentation involves implementing the hybrid augmentation approach, with acoustic transformations for features such as audio level and linguistic transformations for speech data. Results indicate that various Machine Learning models show enhanced performance, achieving up to 0.97 accuracy with the hybrid approach, while the other augmentation approaches achieve lower results, with accuracy ranging from 0.66 to 0.92. In addition, Explainable AI (XAI) strategies are employed to highlight key contributions of significant characteristics such as speech data, audio level, and robot position.
Open Access: Yes
A Spherical Fuzzy ELECTRE III-Based Framework for Evaluating Flood Risk Management Strategies in Vulnerable Watersheds
Publication Name: Boletim Da Sociedade Paranaense De Matematica
Publication Date: 2025-12-29
Volume: 44
Issue: Unknown
Page Range: 1-14
Description:
Flooding is one of the most widespread and damaging natural hazards worldwide, causing significant economic losses, environmental degradation, and risks to human life, particularly in vulnerable watersheds. The multi-criteria decision-making dilemma of managing flood risks in prone watersheds is associated with conflicting economic, social, and environmental objectives. To assess and rank the flood risk management options, this research suggests a single model that should be developed using a mix of the fuzzy analytic hierarchy process and ELECTRE III approaches. The fuzzy analytic hierarchy process is used to capture the uncertainty and subjectivity of the pairwise comparison of decision-makers. Alternative management strategies are ranked using the ELECTRE III technique. The suggested approach is applied to an empirically vulnerable watershed, demonstrating its viability. The suggested fuzzy framework aids decision-makers in selecting the best course of action even before a flood occurs. Watershed managers can use the findings as a scientifically validated tool for resource allocation in flood risk reduction, as they provide a clear and sound hierarchy of strategies that include both structural and non-structural measures.
Open Access: Yes
DOI: 10.5269/bspm.79345
